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[Assistant] Pretty similar to the New York Yankees. They're the team you love to hate. And as a competitor to Larry Ellison, I'm sure he's the person that people love to hate, just given how successful he's been.


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[Assistant] I thought there was no way we could ever replicate it. It would take ages and a plan.


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[Assistant] Our state fair is a great state fair. Don't miss it, don't even delay.


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[Assistant] What's the trouble, blue boy? Ain't you feeling good?


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[Assistant] Is in fact no more than an overgrown canine tooth.


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[Assistant] A lead in the ice, wide enough for them to swim along.


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[Assistant] Chapter 5 Neverland Awakes


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[Assistant] Neverland awoke when its people and animals heard that Peter was returning. The lost boys went to find Peter. The pirates went to find the lost boys.


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[Assistant] The Indians went to find the pirates and the beasts went to find the Indians.


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[Assistant] The lost boys went out dressed in the skins of animals that they had killed. Each boy had a sword. They all walked in one line.


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[Assistant] There were always a different number of boys on the island.


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[Assistant] Before there were many, but some grew up into adults, and Peter didn't allow adults to stay on the island as a lost boy.


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[Assistant] Others left Neverland to return home to their mothers.


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[Assistant] Now, there were only six lost boys on the island. Toodles was the kindest of the boys.


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[Assistant] He had very bad luck. Every time he left the group. Even for just a few minutes.


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[Assistant] He would always return and find the other boys standing over their most recent killed enemy.


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[Assistant] Nibs was almost always cheerful and was a good fighter. Slightly was very proud.


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[Assistant] And Curly was famous for taking the blame for other people's mistakes.


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[Assistant] The last two boys were twins and they were always close together. The pirates were a terrible and violent group.


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[Assistant] The pirate named Seko was famous.


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[Assistant] Because he would always write his name on the bodies of his killed enemies using their own blood.


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[Assistant] The pirate Bill Jukes once killed a hundred men in a single day, and his whole body was covered with dark tattoos.


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[Assistant] Gentlemen Starkey also loved to kill, but was always very clean and wore nice clothes.


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[Assistant] The shit man's smee was very ugly and often would kill people just for fun.


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[Assistant] He carried a weapon called the corkscrew because it looked like a corkscrew that people use to open wine bottles.


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[Assistant] There were two other pirates called Noodler, whose hands had six fingers, and Skylights, who only had one eye.


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[Assistant] The terrible captain, James Hook, was lying comfortably in a carriage that his men pulled. He was the most violent pirate of the group. He was also the largest and darkest.


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[Assistant] He had long black hair that was arranged in curls. Strangely, he had lovely blue eyes. They didn't look like pirate eyes, but when he was killing, they became bright red and horrible to look at.


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[Assistant] Captain Hook was famous for having amazing courage, but he was not always brave. He was scared of two things.


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[Assistant] The great crocodile that lived on the island and looking at his own blood. It was thick and purple instead of red.


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[Assistant] Hook was always smoking two cigars at the same time.


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[Assistant] The most scary and horrible thing about Captain Hook was his missing right hand.


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[Assistant] Instead of a hand, and he used it to kill all his enemies.


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[Assistant] Hook would always kill his enemies in the same way.


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[Assistant] He would cut into his enemy with his hook and then kick their bodies away.


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[Assistant] He would do all this while smoking his two cigars. This was Peter Pan's terrible enemy.


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[Assistant] Both of them were like kings in Neverland, but which of the two would kill the other and win the final battle for the leadership of the island? Many Indians also lived on the island.


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[Assistant] They, like the pirates, were famous for being cruel to their enemies.


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[Assistant] Still, they did not like or hate the lost boys. The Indians only hated the pirates.


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[Assistant] They were always moving silently around the island, hunting the pirates.


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[Assistant] They carried sharp swords and knives, and they covered their bodies with bright war paint.


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[Assistant] He carried the hair of so many pirates that it was difficult for him to walk. The other famous Indian was Tiger Lily.


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[Assistant] She liked to be alone and kept the men away from her with her knife.


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[Assistant] On this night that Peter returned to the island, the Indians were all full of food and wine.


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[Assistant] The beasts that followed the Indians around the island were still thin, angry, mean, and very hungry. These beasts included tigers, lions, and bears.


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[Assistant] They were so hungry that their tongues hung out of their mouths as they followed the Indians.


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[Assistant] The most terrible and famous of these beasts was a huge crocodile.


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[Assistant] This beast was always searching for the same target. That target was Captain Hook.


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[Assistant] You, the reader, will soon learn all about this crocodile.


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[Assistant] After the boys had walked around the small island several times, they decided to rest near their home.


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[Assistant] They became silent because they heard the pirates singing their terrible song.


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[Assistant] The boys quickly jumped into thick small holes, each hole cut in its own trees.


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[Assistant] Each hole was the same size as one of the boys, so that big adults could not get in.


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[Assistant] Including Peter Pan at his own entrance. The seven entrances were all hidden well


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[Assistant] Secco saw nibs running through the forest.


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[Assistant] He lifted his gun up, but Hook used his hook hand to pull down his gun and stop him.


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[Assistant] Cook then turned towards me, his most loyal pirate.


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[Assistant] Hook then sat down on a huge mushroom and lifted up his hook hand.


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[Assistant] Suddenly, Hook jumped up and screamed in pain.


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[Assistant] The two pirates looked down at the mushroom where Hook was sitting. It was the biggest mushroom they had ever seen.


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[Assistant] They tried to pull it out of the ground and it came out easily.


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[Assistant] They saw that the mushroom didn't have any roots. Even stranger


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[Assistant] Smoke was coming out of the place where the mushroom was before.


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[Assistant] The pirates had found the chimney of the lost boy's secret home.


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[Assistant] When enemies were close, the lost boys always covered the chimney with a mushroom. The boys were inside speaking loudly.


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[Assistant] They were making plans to welcome Peter Pan home.


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[Assistant] The pirates listened to their conversation for a while.


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[Assistant] They all smiled and put the mushroom back in its old place.


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[Assistant] They then searched around and discovered the holes in the trees.


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[Assistant] Laughs me. His eyes were bright with excitement.


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[Assistant] Exclaimed Smee, and all the pirates began to sing. Just as they finished singing, they heard another sound. Tick, tick, tick.


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[Assistant] Said Hook, terrified. He ran away quickly, followed by Smee and the other pirates.


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[Assistant] Said Curly. In reality, it was not a bird. It was Wendy. Wendy was now quite close to the boys.


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[Assistant] They quickly jumped down into their secret home and got their bows and arrows.


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[Assistant] Toodles came back fastest and prepared his bow. Move, Tink, he shouted. He then fired an arrow into Wendy's chest.


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[Assistant] Wendy fell straight down to the ground with a loud sound.


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[Assistant] On the third day, I was better. On the fourth, I could speak, move, rise in bed and turn.


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[Assistant] Hannah had brought me some gruel and dry toast about, as I supposed, the dinner hour. I had eaten with relish. The food was good.


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[Assistant] Void of the feverish flavour which had hitherto poisoned what I had swallowed. When she left me, I felt comparatively strong and revived. Ere long, satiety of repose and desire for action stirred me. I wished to rise, but what could I put on? Only my damp and bemired apparel, in which I had slept on the ground and fallen in the marsh. I felt ashamed to appear before my benefactors so clad. I was spared the humiliation.


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[Assistant] On a chair by the bedside were all my own things, clean and dry. My black silk frock hung against the wall.


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[Assistant] The traces of the bog were removed from it. The creases left by the wet smoothed out. It was quite decent.


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[Assistant] My very shoes and stockings were purified and rendered presentable.


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[Assistant] After a weary process, and resting every five minutes, I succeeded in dressing myself. My clothes hung loose on me, for I was much wasted, but I covered deficiencies with a shawl, and once more, clean and respectable looking,


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[Assistant] No speck of the dirt, no trace of the disorder I so hated, which seemed so to degrade me, left. I crept down a stone staircase with the aid of the banisters, to a narrow low passage, and found my way presently to the kitchen.


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[Assistant] It was full of the fragrance of new bread and the warmth of a generous fire. Hannah was baking.


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[Assistant] Prejudices, it is well known, are most difficult to eradicate from the heart whose soil has never been loosened or fertilized by education. They grow there firm as weeds among stones. Hannah had been cold and stiff indeed at the first. Latterly she had begun to relent a little.


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[Assistant] And when she saw me come in tidy and well dressed, she even smiled. What? You've got up, she said. You're better then. You may sit you down in my chair on the half stone if you will. She pointed to the rocking chair. I took it.


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[Assistant] She bustled about, examining me every now and then with the corner of her eye.


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[Assistant] Turning to me as she took some loaves from the oven, she asked bluntly, Did ye ever go a-begging afore ye came here? I was indignant for a moment, but remembering that anger was out of the question, and that I had indeed appeared as a beggar to her, I answered quietly, but still not without a certain marked firmness. You are mistaken in supposing me a beggar.


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[Assistant] I am no beggar any more than yourself or your young ladies. After a pause, she said, I don't understand that. You've like no house nor no brass, I guess.


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[Assistant] The want of house or brass, by which I suppose you mean money, does not make a beggar in your sense of the word. Are you book-learned? she inquired presently. Yes, very. But you'd never been to a boarding school? I was at a boarding school eight years. She opened her eyes wide. Whatever cannot you keep yourself for then?


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[Assistant] I have kept myself, and I trust she'll keep myself again. What are you going to do with these gooseberries? I inquired, as she brought out a basket of the fruit. Macamint de pies. Give them to me, and I'll pick them. Nay, I do not want ye to do nought. But I must do something. Let me have them. She consented, and she even brought me a clean towel to spread over my dress. Lest, as she said, I should muckie it.


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[Assistant] You've not been used to servant's work, I see by your hands, she remarked. Haven't you been a dressmaker? No, you are wrong. And now, never mind what I have been.


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[Assistant] Don't trouble your head further about me, but tell me the name of the house where we are. Some calls it Marsh End, and some calls it Moore House.


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[Assistant] You obstructed an officer of my guard in the pursuance of his duty. Were you an ordinary man, Lord Byron, you would not be sitting here now.


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[Assistant] Have you enjoyed your time in Tokyo? I had thought myself in Greece.


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[Assistant] This is Sheila Graham with the latest news of the shocking Williams murder case. While beautiful Irene Williams is held for trial, charged with conspiring to kill her late husband, the police are searching for James Torrance, believed to be the lover of Mrs. Williams and her partner in the horrible murder of Walter Williams.


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[Assistant] You are all somewhat in awe of you, my lord. You have found that rare treasure.


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[Assistant] Dozens of women from Canada, the US and the Bahamas have now come forward to accuse Peter Nygaard of sexual assault, including rape. Most of the allegations are from recent years.


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[Assistant] But the Fifth Estate has also uncovered cases that are decades old, including what you're about to hear from this woman who says that Nygaard raped her in 1980.


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[Assistant] And that when she tried to go public with her story years ago,


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[Assistant] Powerful Winnipeggers made sure all the details were kept secret. That is, until now. Peter Nygaard began his fashion empire in Winnipeg.


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[Assistant] By the 1990s, he was one of the city's most prominent business leaders.


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[Assistant] Reporter Catherine Mitchell covered Nygaard for the Winnipeg Free Press.


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[Assistant] But a darker side of Peter Nygaard was about to emerge. In 1996, Mitchell and another journalist


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[Assistant] Learned about a story with the potential to shake up Winnipeg and Niagara.


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[Assistant] Mitchell reported that three of Nygaard's female former employees


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[Assistant] had launched sexual harassment complaints against him with the Manitoba Human Rights Commission.


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[Assistant] By Monday, Mitchell's voicemail at the free press was full.


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[Assistant] One of the names she got was a former Nygaard employee living in Denmark, Janne Larsen.


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[Assistant] This past winter, we went to Denmark to track her down and to hear her story for ourselves.


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[Assistant] But in the early 1980s, she was a single mother from Denmark with a flair for fashion, hoping to start a new life in Canada.


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[Assistant] Eventually, she landed a job in Winnipeg, working for Niagara.


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[Assistant] But she still remembers being shown to her room by the hotel manager.


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[Assistant] Reassured, she says she fell into bed exhausted by the overseas flight, but then in the middle of the night.


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[Assistant] Back in Canada, she says the atmosphere at work was changing. The Peter Nygaard began to harass her.


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[Assistant] Finally, in the summer of 1981, Yonah Larson says she was forced out of her job with Peter Nygaard's company.


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[Assistant] Iyana had confided in her friend and fellow employee Dale Drefs about what took place in Asia.


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[Assistant] Distraught, she eventually threatened to go to the press and the police.


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[Assistant] Then she decided to tell it all to the Winnipeg Free Press. Catherine Mitchell did the interview. The question is, why didn't it appear in print? The newspaper's publisher at the time was Rudy Retikop.


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[Assistant] People he calls Nygaard's compatriots and Winnipeg kingpins. He says he worried the reporting might be sensationalist. What's more, Peter Nygaard threatened a lawsuit.


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[Assistant] Catherine Mitchell says she and the majority of the newsroom disagreed, but their boss killed the coverage anyway.


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[Assistant] Today, former publisher Rudy Retikop says that was a different time, and he now admits that maybe we should have proceeded with the Nygaard story. Recently, Peter Nygaard's lawyer sent us a statement


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[Assistant] Saying Mr. Nygaard was not involved in any attempt to stop the publication of a story about unreliable, false, and malicious allegations.


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[Assistant] In 1982, Janna Larsson and her daughter Zita returned to Denmark. Janna eventually married, is a grandmother.


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[Assistant] And lives a quiet life in a town on the shores of a northern fjord. And now, almost 40 years later, Jana's full story is finally being heard.


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[Assistant] Alright, give me the blue coat and the polka dot tie. And your tan shoes? And the tan shoes. Yes, sir.


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[Assistant] Ah, they're lovely. You know, too few ladies bring me roses these days.


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[Assistant] You're just as beautiful as ever. The only time I mind not having eyes is when you're around.


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[Assistant] Uh-huh. A great man indeed is one of the best scientific minds we have today. And heaven knows we need him.


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[Assistant] I'm glad you came to me, Norman. I think your father would have expected me to help you.


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[Assistant] Oh, I can do that for you anytime you want. Who is the victim?


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[Assistant] The dreariest leading man you ever had. And when you told me that you were engaged to him, I wanted to take you over my knee.


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[Assistant] My dear, you seem to be forgetting one thing. You're not just another woman that's been treated badly by Gerente. You're this girl's mother now. And your husband expects you to look after her. She's your responsibility.


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[Assistant] Well try it. If it doesn't work, we'll, we'll figure out something else.


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[Assistant] Wait a minute. In case I've got to call you, what is your phone number?


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[Assistant] Did it ever occur to you that they might have used the rug to carry the body out? Right.


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[Assistant] Get all of the janitor. See if he can get a line of Geretti's friends who might have visited him tonight.


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[Assistant] Don't mention anything about a stiff you want us both thrown into jail. Don't you know what we're doing may be turned conspiring to defeat justice?


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[Assistant] I bet you've been questioned a lot. You know how to keep your mouth shut, don't you?


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[Assistant] All right. See if I can guess. You planned to kill Gerente someplace else. There was a sudden quarrel.


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[Assistant] Now you had to do it here to shut him up. You didn't want the body found here, so you took it away in a rug. You rang the bell that time, Max. Now maybe you'll tell us why you didn't want the body found here.


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[Assistant] All right. That means they're gonna make a trip. Friday.


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[Assistant] Why yes sir, she does. Ah, we made it boy, come on. Excuse me sir, but may I ask who you are? Oh, you.


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[Assistant] Well, send him away. I haven't seen you for two years. I want servants gawking in on a family reunion.


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[Assistant] Uhm, Olivero, will you turn the coverings down on my bed, please?


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[Assistant] I've got a little job I want you to do. I want you to sew a button on that shirt.


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[Assistant] Divine brawl. Oh, Otgarone 1904, if I'm not mistaken. Ah, a monumental vintage


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[Assistant] Sure, I read with my fingers. That's the blind man's advantage. He can read in bed with the lights out, or even under the covers if it gets cold. Oh, I, I see, sir.


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[Assistant] Now there's the jack of diamonds, there's the eight of clubs, there's the four of hearts, there's the deuce of spades, and the eight of spades. Is that right?


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[Assistant] Now I'm going to deal out three piles of cards.


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[Assistant] I want you to look at the top card on the end pile.


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[Assistant] You got it? Yes, I got it. Remember them now. Now, uh, put your hands flat on the cards. Go ahead. This is really a great trick. Let's see. Now the trick is this.


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[Assistant] Norma, who is this man? Oh, you must be Stephen Laurie. Well, I'm your wife's long lost uncle.


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[Assistant] Just a moment, Miscot. There's no need for all this and you know it. Once you get a hold of the complete formula, you won't let us live ten minutes.


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[Assistant] Yes, you slipped up on the killing of Paul Girardi.


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[Assistant] It may even surprise you to know that I have positive proof that you ordered him to be killed.


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[Assistant] Just before you cut the phone wires, I got word to him that his wife was murdered. And who did it? Gabriel is out for revenge.


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[Assistant] If I'm not home in time for breakfast, the police will be notified and Gabriel will start talking.


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[Assistant] Life is sweet, Miss Scott, even to a blind man. If you let us go, I'll try and persuade Mr. Lorry to turn over the balance of the formula to you. But mind you, we must have positive proof that you will let us go unharmed.


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[Assistant] I want Rossmore, 555. My number? Murray Hill 44598.


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[Assistant] Hello, Russmore 555? This is Gabriel. That you, Vera? Yes, I'm all right. I know, but somebody has to do the dirty work. It's part of the job. All right, I'll be back soon. Now, baby, stop worrying about me.


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[Assistant] I've been through the mill. A blind man and another guy caught me in Geretti's apartment.


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[Assistant] It's like so, it's always like sexy and it's like smooth and it's very like Frank Sinatra and Johnny Mathis.


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[Assistant] There's a song called, So Sorry by Faiz that was just, it's like the most like, you just hear it, the first line is like, I'm sorry, it's like ahhh.


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[Assistant] A lot of this album I really want to be up for interpretation because I will say every single line in that song has a certain meaning to me. There's so many lines that sound like something else or they, you know, they mean what they don't mean and they don't mean what they mean. And I just like the idea that people aren't gonna know what it's about.


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[Assistant] I begged him not to go in the first place, but nobody listens to me. Not until they get into trouble. Then they come. Aunt Marge has a large pocketbook. Oh Aunt Marge, what's the use of all that? Now it's two o'clock and- Just like your father. Always interrupting, refusing to listen, but you will listen this time.


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[Assistant] And the third was a little bitty chair.


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[Assistant] Uhm, I don't know whether to believe this or not, but uh, one of our producers here reckons that you actually recorded it underwater, that line.


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[Assistant] There is a poem of yours, never yet published, but more talked about than half the poems that are.


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[Assistant] Describes the Pleasure Palace at Kutla Khan. I would like to see it published. I could run to £100.


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[Assistant] I would have paid Wordsworth twice that sum for his last poem if only he'd left it unfinished.


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[Assistant] I was told it never leaves your person. Is it not in this pocket here?


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[Assistant] With facilities like hotels and research centers, a new reality is taking shape because it has to.


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[Assistant] I'm a dreamer, but I'm also extremely


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[Assistant] More than 200 million. 200 million.


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[Assistant] Well, yes, Alan, it would, but, well, there's my patient.


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[Assistant] Your supper ought to be ready, if Betty's found a tin opener.


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[Assistant] Alan, I didn't kiss George. He kissed me. And perhaps I don't want you to apologize.


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[Assistant] All the locations of PCC Airforals in Eastlake, Mender, Wycliffe, and Unerva are hiring for all positions starting at $18 an hour.


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[Assistant] Using only a gif. What's your initial reaction to the enjoku extension? Steve, let's cut right to the next video here and we'll get to four internet reactions.


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[Assistant] Hey, first bet today, Guardians, one and a half run favorites over the Royals, Quantrill on the mound, and T-Bush the lone anti-Guardian.


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[Assistant] I think they're gonna win. Go ahead. Number two. Another disagreement here.


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[Assistant] With Serena in-house, we did top 5 NBA cities, and as always, The Ultimate Five is sponsored by our favorite


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[Assistant] Roundstone Insurance! Roundstone! Roundstone! For more than a decade, Roundstone has been saving small and mid-sized businesses money, an average of 20%. Contact us today to learn how you can get great health care benefits that your employees will love and save money too.


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[Assistant] I have been to NBA games in 13 different cities. Not to brag, but it's probably the coolest thing I've done. So it's the top 5 of the 13.


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[Assistant] Philadelphia. Eli is now the video coordinator for the 76ers. You have a little biased here. See, that's the thing.


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[Assistant] Alright, number two, number three. Number three?


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[Assistant] Cleveland, Ohio slash Boston. One's a round stone pick, one's a real pick.


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[Assistant] There you go. We had a sponsor pick here. Cleveland, Ohio had to be on the list.


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[Assistant] So if you didn't see this came out epic on Saturday, there was a slap hurt around the world. Take a look. I'm going to play this a couple of times. Ooh, it's Tommy fam and jock Peterson over a fantasy football beef.


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[Assistant] Peterson put a player on IR. Two baseball players for those who didn't like it.


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[Assistant] We'll be ready soon. Ready for you to see, that is. I value your opinion always.


[User] Please transcribe this text

[Assistant] Now one link is going to be to show the current polls and the other link is going to be to show a form to add a new poll. So we're creating this tab system so that when we click on the show polls tab, it's going to show the poll list component. And when we click on the create new poll tab, it's going to show the create new poll form component.


[User] Please transcribe this text

[Assistant] So we're just going to focus on creating this tab system in this tutorial. Now there's loads of different ways you could create this. There's not one way to create something. This is just one way.


[User] Please transcribe this text

[Assistant] And the way I'm going to do it is by first of all, over here, declaring a couple of variables. So let me do a tabs comment first of all, so we know what all this is. Then I'll create a variable called items and this items variable will be an array. And in the array, we're going to store what items or what text links we're going to have in the tab system. So we're going to have one tab that says.


[User] Please transcribe this text

[Assistant] New poll, and we're gonna click on that to create a new poll.


[User] Please transcribe this text

[Assistant] So now we also need to say which one of these tabs is going to be active to begin with. So I'm going to say let and I'm going to call this one active item and set it equal to one of these things right here.


[User] Please transcribe this text

[Assistant] Well, by default, I want to show the current polls on the screen. So I'm going to copy that and paste it here. And these two must be exact, not current polls with a lowercase p. They must be exact because we're going to check the difference between them later when we decide what content to show and we'll see that.


[User] Please transcribe this text

[Assistant] Okay, so we have these two items now. The next thing I'd like to do is create a tabs component so we can pass these in as props. So in the source folder, I'm going to create a new folder and I'm going to call this shared, not in capitals.


[User] Please transcribe this text

[Assistant] So, we're gonna place it in this shared folder. Let's create a new file called tabs.


[User] Please transcribe this text

[Assistant] Oops, I've spelt that completely wrong. Let me rename that.


[User] Please transcribe this text

[Assistant] So it's tabs.svelte. Okay then. So inside here, let's first of all create a script tag and we'll come back to that in a second. Then we need some kind of HTML template. So we'll start that off with a div with a class of tabs.


[User] Please transcribe this text

[Assistant] And we'll come back to that in a second as well. And then finally, we need a style tag in case we style this up, which we will do.


[User] Please transcribe this text

[Assistant] All right then. So our tabs are essentially going to be a list of items. In our case, just a list of two items. These things right here.


[User] Please transcribe this text

[Assistant] So if I'm creating a list and I typically use a UL, you don't have to.


[User] Please transcribe this text

[Assistant] I'm going to use a UL and now what I need to do is cycle through the actual tabs that we're going to have an output, a bit of HTML for each one, an actual tab. Now in order to do that, I have to pass the tabs in as a prop, these things right here, the items.


[User] Please transcribe this text

[Assistant] So let us first save this for now and come over to app.svelte and import the tabs component. So I'm going to.


[User] Please transcribe this text

[Assistant] Duplicate that and change this to tabs then this to the shared folder.


[User] Please transcribe this text

[Assistant] And this should be tabs as well. So we've imported that and we're going to place the tabs probably inside the main thing right here. So let's do the tabs.


[User] Please transcribe this text

[Assistant] Now I'm also going to pass in the active item as well. So.


[User] Please transcribe this text

[Assistant] We'll say active item because I'm sure we'll use that inside the tabs component at some point.


[User] Please transcribe this text

[Assistant] Okay, so we're passing that data in as props. Now inside the tabs component itself, we can accept those. So let me do that inside the script. I'm going to say export, which we need to do in front of any variables, which we're accepting as props.


[User] Please transcribe this text

[Assistant] And then let items and then underneath that we need to also accept the other variable, which was, I think, let me have a look. Um, active item. Yep. Okay. So export let active item.


[User] Please transcribe this text

[Assistant] So we have those two variables now and we can use them. Now inside the UL I want to output an li tag for each item. Remember items is an array so we can cycle through these and output a bit of template for each one. So let us now do an each loop.


[User] Please transcribe this text

[Assistant] We do that by using curly braces, hash, each, then what we want to cycle through, items, and then I'll name each one item as we iterate through. So let's close that off by saying forward slash each at the end. And then inside, I want to output an li tag for each item.


[User] Please transcribe this text

[Assistant] So let's do that, and then inside the ally tag, I'm gonna actually output the item itself, the text of it, so I can just use this variable.


[User] Please transcribe this text

[Assistant] So let me first of all do a div because we're going to use this to apply a class later and then output an item inside it. All right. So now all we're doing is cycling through the items and output an ally tag for each one. So if I save this now, we should see both of those are right here. Awesome.


[User] Please transcribe this text

[Assistant] Okay then, so what do I want to do next? Well, first of all, I want to say whether this current item is active. And we can use this active item to determine that, to apply a conditional class. We've seen how to do that in the past. So to apply a conditional class, I can say class.


[User] Please transcribe this text

[Assistant] If we cycle through items and currently we're cycling through this one, we're going to say, okay, is current polls equal to active item, which is current polls. This would be true. Therefore this is true. So we apply the class of active to this div and then we can style this a little bit differently to show the user that this is an active item.


[User] Please transcribe this text

[Assistant] When it cycles through the second one, add new poll. Well, this is not equal to the active item current polls. Therefore it doesn't apply this class of active. So this is purely just to style this differently. So a user knows which item is active and what content they're seeing on the page. So down here, let me just add the active.


[User] Please transcribe this text

[Assistant] Nine, one, B, four, two. And that should be a reddy colour. Yep, this red colour here.


[User] Please transcribe this text

[Assistant] It's gonna be two pixels, and it's gonna be solid, and it's gonna be the same colour, so let me copy that bad boy.


[User] Please transcribe this text

[Assistant] And paste it right here. And then finally the padding hyphen bottom is going to be about eight pixels. Okay. So if I save this now, we should see this styling on the active tab, which is current polls. Okay. Still looks pretty pants at the minute, but we will make this look better. In fact, we'll do that first of all.


[User] Please transcribe this text

[Assistant] So let's style this div first of all. So tabs, oops.


[User] Please transcribe this text

[Assistant] So tabs and then inside we'll say margin.


[User] Please transcribe this text

[Assistant] Bottom is gonna be about 40 pixels. That's just so that the content underneath is given some breathing room. Then we'll do the UL.


[User] Please transcribe this text

[Assistant] And this will display as flex. So this is going to automatically put the ally tags left to right and take up the available room on the row of content. If you want to learn more about flex, I do have a whole series on flex box, but I don't want to dive too deeply into it now because we're talking about svelte. Then I'm going to say justify.


[User] Please transcribe this text

[Assistant] Is going to be none. That's to take away the little circle icons that we get on li tags automatically. So let me check this out so far.


[User] Please transcribe this text

[Assistant] Alright, looking better. Now we just need to add a bit of breathing room between those two things and also maybe change the font size. So let me now style the LI tags themselves. The margin is going to be zero top and bottom, 16 pixels left and right.


[User] Please transcribe this text

[Assistant] The font size, about 18 pixels, just crank it up a little bit. The colour is gonna be kind of like a grey colour by default. Obviously the active one overrides that.


[User] Please transcribe this text

[Assistant] And then we're going to say the cursor is pointer just so a user knows they can click on it. So let's save that and preview. Okay. Looks good. So this is the active item at the minute, but when we click on these, nothing currently happens. Now, when we click on add new poll, what I'd like to do.


[User] Please transcribe this text

[Assistant] Is then emit a custom event from this component and send up the item as data that was clicked on to the app component. Then we can take that item and we can update the value of active item to be the item. Does that make sense?


[User] Please transcribe this text

[Assistant] So let me first of all import the create dispatcher because we need that to dispatch a custom event. So I'm going to say import and it's create event dispatcher.


[User] Please transcribe this text

[Assistant] We've seen all of this in the past when we talked about custom events and that is from the Svelte library. All right. So.


[User] Please transcribe this text

[Assistant] Now we have that, we can go ahead and create a dispatch function. So I'm going to do that right underneath. I'm going to say const dispatch.


[User] Please transcribe this text

[Assistant] Is equal to create event dispatcher like so and now we can use this dispatch function right here to dispatch a custom event every time a user clicks on one of these li tags. So I'm going to say right here on click.


[User] Please transcribe this text

[Assistant] Let me do that and inside the function, I'm going to say dispatch and we want to dispatch an event called tab change, but you can call it what you want. And then the data we're sending with that custom event to the parent is going to be the item. So whatever item we're currently iterating.


[User] Please transcribe this text

[Assistant] So when we click on this, we dispatch this custom event tab change and we can listen for that event inside the app component where we output the tabs. So I could say right now over here on, oops.


[User] Please transcribe this text

[Assistant] And it's going to be an arrow function like so. And all we want to do inside here is reset the active item equal to whatever item we send right here in the event.


[User] Please transcribe this text

[Assistant] Remember we can grab the data that we send by using the event object and the detail property on that event. So I could now say that active item is going to be equal to e.detail, like so. And e.detail is just the item we send along right here. Okay.


[User] Please transcribe this text

[Assistant] So now we've done that, when active item changes, say we click on add new poll right here, we send add new poll as the item value.


[User] Please transcribe this text

[Assistant] It emits the event. We'll listen to it. We call this function and we set active item now to add new poll. So when this active item changes.


[User] Please transcribe this text

[Assistant] What we're sending down here is obviously updated and so therefore active item now is create new poll and so therefore the active class will be given to the second Li create new poll, right? Because then the item is equal to that.


[User] Please transcribe this text

[Assistant] So let's save everything and just test this out. I'm going to go to add new poll and it switches. If I go to current polls, it switches, et cetera. So this tab system is now working and that's awesome.


[User] Please transcribe this text

[Assistant] No, we want to show content dependent on what the current item is. And we're going to nest that eventually inside app.svelte, the root component.


[User] Please transcribe this text

[Assistant] So how do we do that exactly? Well, we could check whatever the active item is down here in the main tag using an if statement. And then if it's current polls, we'd show the current polls component. If it's add new poll, we'd show the add new poll component.


[User] Please transcribe this text

[Assistant] So let me just quickly demo this, but we'll make it better later on. I'm going to say hash if to do an if check. And I'm going to say if active item is triple equal to current.


[User] Please transcribe this text

[Assistant] So this has to be exactly the same as whatever the active item might be. So this or this, right? And if that's true, then we're going to output a paragraph that says, poll list component goes here.


[User] Please transcribe this text

[Assistant] And now let me do an else statement. So colon else and then if, and if active item is triple equal to add.


[User] Please transcribe this text

[Assistant] All right then, so let's try this out. So we can see poll list component goes here because this is the active item. If we change it, then we can see new poll form component goes here.


[User] Please transcribe this text

[Assistant] So now we're switching between these two bits of content dependent on the active item.


[User] Please transcribe this text

[Assistant] And the great thing about this is that this tabs thing right here is reusable. So if you wanted to use this elsewhere on your website, you could do now because we don't hard code anything in here, any tabs. All we do is we set up an items array and we pass that in as a prop.


[User] Please transcribe this text

[Assistant] Right here along with the active item. So if we wanted different items going into tabs elsewhere, we'd set up different items like this, a different active item and just reuse the tabs component, passing those things in.


[User] Please transcribe this text

[Assistant] So now we have that working. What I'd like to do in the next video is start on this component right here, which is going to be the farm component to add a new


[User] Please transcribe this text

[Assistant] You make everything so simple. Just what do you mean?


[User] Please transcribe this text

[Assistant] I know. It was not as an individual, but as the party.


[User] Please transcribe this text

[Assistant] Stop, please. I can stand anything, but you're trying to behave like a gentleman.


[User] Please transcribe this text

[Assistant] Just a minute. I want to tell you something.


[User] Please transcribe this text

[Assistant] There are only two kinds of people, but they're not the two kinds that you talk about. They're just male and female. Goodbye.


[User] Please transcribe this text

[Assistant] I think I'm carrying part of it away in my boots. Any charges? No. Compliments of the house. Whoa, hold it. Oh, a little hot. Very nice.


[User] Please transcribe this text

[Assistant] Fine neck. She carries herself well. She's got breathing. Glad you like her. You don't mind if I look? You can look.


[User] Please transcribe this text

[Assistant] You're quite an admirer of horse flesh, aren't you? Yeah. It's my business. Your first trip to the coast? First trip. Out to see the elephant, huh? Come again? Oh, that's what we call doing the town. Oh. Well, something like that. That owes me $500 I'm here to collect. Ever hear of a place called El Dorado? Who hasn't? It's about the hottest spot on the Barbary coast.


[User] Please transcribe this text

[Assistant] They call me Duke Ferguson, Montana. If you're ever traveling through and want to pat your feet in fresh water, look me up. I remember that.


[User] Please transcribe this text

[Assistant] Well, I'll have to make my father chairman of the board. Father, send another 200 in his name.


[User] Please transcribe this text

[Assistant] Just wanted your opinion. Do you suppose you could fill your place in the shop?


[User] Please transcribe this text

[Assistant] That's why I want you to meet each new prospect that comes to San Francisco.


[User] Please transcribe this text

[Assistant] Oh, uh, Martha. Tell Joe to put another glass in that window.


[User] Please transcribe this text

[Assistant] I paid the man who delivered him. And won it right back at roulette.


[User] Please transcribe this text

[Assistant] A very particular type. I'm afraid that type is rather expensive. So expensive in fact that I reserve it for my own team. Five hundred dollars is a lot of money. Not at El Dorado. Suppose we make it a thousand.


[User] Please transcribe this text

[Assistant] It's all right for the afternoon. Get something better for the evening. I was just thinking.


[User] Please transcribe this text

[Assistant] That's one bet you'd win the night.


[User] Please transcribe this text

[Assistant] Would you teach me something about the game, lady? I never played before.


[User] Please transcribe this text

[Assistant] Gamber now, Calico. Maybe you are faster.


[User] Please transcribe this text

[Assistant] As a matter of fact, I might decide to buy this place and settle down. Not a bad idea, but you know...


[User] Please transcribe this text

[Assistant] Remember I told you about the blue lake at the foot of the snowy mountains? Well, there's a cabin near that lake.


[User] Please transcribe this text

[Assistant] The Morel family is San Francisco. It's very mellow, gracious, touched with the charm of the old world. Oh, there have been new additions to be sure, things that are typically American.


[User] Please transcribe this text

[Assistant] Aren't you a bit out of your class tonight? Oh, I'm not playing. I'm sorry. I thought we might provide you with some action. So did I. So I brought this along, just in case. Exactly what is it you want, Jill? Action.


[User] Please transcribe this text

[Assistant] I lost a few dollars in El Dorado. You lost nothing except the few hundred dollars that I gave you. Then let's put it this way. I didn't win anything. This time you expect to? Could be.


[User] Please transcribe this text

[Assistant] Some reason I'd rather not play against you, Joe. Suppose we say that tonight you're my guest. I'm not your guest, Tito. As you wish. Excuse me, I'll get a table.


[User] Please transcribe this text

[Assistant] Well you picked a fine time to tell me.


[User] Please transcribe this text

[Assistant] The man was wrong. Uh, oh, shake hands with Wolf Wiley.


[User] Please transcribe this text

[Assistant] Hiya, trouble. Mr. Morell, how's your table ready, sir? Walker.


[User] Please transcribe this text

[Assistant] Wait a minute. Don't you want to see me win the first part of the evening?


[User] Please transcribe this text

[Assistant] It was Shanghai Kelly who took it the hard way last night when Duke Fergus sat in for a few hands of stud. Shanghai asked for it and Shanghai... Are the rest the same? Just about. Thanks, dear.


[User] Please transcribe this text

[Assistant] Where is he tonight? At Calico Gyms. I warned Calico not to deal to Fergus. Calico isn't dealing. Joe Disco.


[User] Please transcribe this text

[Assistant] Here, have another to recover. I still think we should get in the works. You talk too much. Calico says he'll wait until midnight if you want to see the show. Alright, let's go.


[User] Please transcribe this text

[Assistant] No. Oh no. The last time you looked over my shoulder, I put her lost my- I put her lost.


[User] Please transcribe this text

[Assistant] Take a look around. You don't think I'd do all this and then be stopped by a contract. One that undoubtedly has mice in every closet.


[User] Please transcribe this text

[Assistant] A dollar gets you a main coat. When that curtain goes up, you'll have a new contract.


[User] Please transcribe this text

[Assistant] Hello. Hello. Blainston, Callico and Kelly? You. Not enough cuspidors? One reason I like you, Duke, is because so often we think alike. Do we?


[User] Please transcribe this text

[Assistant] Well, how is the silver dollar? A raid open. If you ever do. Which I intend to do, about the middle of April. Cut your leading lady.


[User] Please transcribe this text

[Assistant] How much for Flaxen's contract? Just one dollar more than you can afford.


[User] Please transcribe this text

[Assistant] That, for instance? Not interested. I've offered to buy that contract, Tito. I also talked to my lawyer. He tells me it isn't worth a quarter of flax and takes you to court. I could have told you that.


[User] Please transcribe this text

[Assistant] And for less money, perhaps she's got what she wants. What's your price? Not a chance, Duke. And without flaxen, you're dead. There isn't another girl in town that would make a silver dollar payoff. Maybe.


[User] Please transcribe this text

[Assistant] You gonna take that sitting down too? Go away, stupid.


[User] Please transcribe this text

[Assistant] Hello. Is Mr. Fergus? Are they still coming in? Yes.


[User] Please transcribe this text

[Assistant] First time I ever wore them. When you sit down, do you give it this or this?


[User] Please transcribe this text

[Assistant] Lansing's 93 for this table. Compliments of the house. Aren't you pouring it on a little Duke? Maybe not. We'll be partners soon. In what? Barbary Coast Incorporated. Maybe I'd rather compete. What makes you think you can?


[User] Please transcribe this text

[Assistant] Drag up a chair and sit down, we'll talk it over. We'll talk it over later. Take off your hats.


[User] Please transcribe this text

[Assistant] Well, you certainly kept your word. This is bigger and better than anything in town. You think so?


[User] Please transcribe this text

[Assistant] Oh, uh, Flagson will be on soon enough. Ah, but not soon enough for Tom.


[User] Please transcribe this text

[Assistant] Four times, keep the record straight. Fred here is still one up on it.


[User] Please transcribe this text

[Assistant] What I called in the plumbers. Plumbers? That's right. They're installing a sunken tub in your dressing room at El Dorado.


[User] Please transcribe this text

[Assistant] Do you? Then go over there like a good little girl and sit down. We've got a visitor.


[User] Please transcribe this text

[Assistant] Wolf? Aren't you ashamed of yourself? Walking into a lady's boudoir unannounced? Didn't you know I would? Yes. That's what I thought.


[User] Please transcribe this text

[Assistant] She must be a fine woman to have so many people who love her. Well, how is she? Oh, she'll live. She'll live. And someday perhaps she may walk again. I don't know. I've done all I can. I would like to do more. There's so many. I believe they're ready now.


[User] Please transcribe this text

[Assistant] Where's Tito? Trying to save Pacific Street. What's left of it? That's the way it went. Some lost everything, some lost nothing.


[User] Please transcribe this text

[Assistant] Just about under control. Can't go beyond the warehouse and Battery Street. Good. How is she? That gal is gamer than a bandy rooster. When have they taken her home?


[User] Please transcribe this text

[Assistant] Why not agree with her? Sometimes it's safer. I wish you would take it. Too much excitement.


[User] Please transcribe this text

[Assistant] He's backing Jim Phelan for mayor. How do you do? How do you do? Sit down. I'll take mine standing. I don't want to hold up your meeting. It was called for your benefit. San Francisco's not my city. Would you like it run by Tito Morel?


[User] Please transcribe this text

[Assistant] Well, I'm sorry, gentlemen. I don't care who runs it. I'm heading for Montana.


[User] Please transcribe this text

[Assistant] What right have you to come barging in here? Priority? I had the lady to select this home. Alright, there's your man.


[User] Please transcribe this text

[Assistant] It's just that we remembered what you said at the opening of the Silver Dollar about Barbary Coast Incorporated. Yes?


[User] Please transcribe this text

[Assistant] Well, it's along the same lines. We're sort of reorganizing, and Tito here figured you'd want in. Don't deal me in on this. You don't approve, eh, Tito? Frankly, I think you're a chump. A little lucky at times, but hardly the type for a job like ours. Didn't I hit you hard enough the last time you ran off at the mouth? Evidently not. Or, uh, didn't you hear who Flaxen wants to see? Did a rain check go with that offer of yours? You mean you're with us?


[User] Please transcribe this text

[Assistant] For a better San Francisco? No, I'm again him. So am I. Thank you, Wolfie. Satisfied?


[User] Please transcribe this text

[Assistant] Got the knob hill returns? Not in yet. They should be here. Don't worry, they're safe. How's it shape up? Oh, just about the way we figured. Kido did a good job along the coast though. He carried these for Schmitz with a safe margin. We broke even here. Give us five more precincts and we're in. You've got them.


[User] Please transcribe this text

[Assistant] Now, there comes a time when... You ought to sell gold bricks. You'd make a fortune.


[User] Please transcribe this text

[Assistant] Your competition can copy you or just drop their margins a little bit and compete with you. Um, I mean, faster, it's always better to be faster as we talked about before, but building a whole company around faster, your competition could just be motivated to go faster.


[User] Please transcribe this text

[Assistant] Each eye is engraved here with a special needle, bit by bit.


[User] Please transcribe this text

[Assistant] Each client is different. One says, I want a square one. I don't need any figures. Others tell me, I don't like square ones. I need something like this.


[User] Please transcribe this text

[Assistant] Everyone has their own taste, their own vision. This has been a tradition for generations. I guess somebody thought it up. It makes sense.


[User] Please transcribe this text

[Assistant] Gravestones are for the living. They're for grandchildren and great-grandchildren.


[User] Please transcribe this text

[Assistant] That's how we commemorate the dead.


[User] Please transcribe this text

[Assistant] My work has to do with people's souls, you know what I mean?


[User] Please transcribe this text

[Assistant] I take it all to heart, and at such moments I feel what I do is somehow noble.


[User] Please transcribe this text

[Assistant] But I've had colleagues who had to quit after one day at the cemetery.


[User] Please transcribe this text

[Assistant] There's something mystical about it. Mystical.


[User] Please transcribe this text

[Assistant] Chapter 7 Good News and Bad News for David


[User] Please transcribe this text

[Assistant] The more brightly Dora Starr shone down on me. I'm sure I considered her a beautiful angel, far above the rest of us poor humans.


[User] Please transcribe this text

[Assistant] And I had no real hope of persuading her to love me.


[User] Please transcribe this text

[Assistant] Peggy had come to London with me, as she was feeling rather lonely after Barkas's death and Daniel's departure.


[User] Please transcribe this text

[Assistant] And I could not stop myself telling her all about Dora. She was very interested and most encouraging.


[User] Please transcribe this text

[Assistant] The young lady should be very proud to have such a handsome, intelligent young man as you, Master David," she cried.


[User] Please transcribe this text

[Assistant] And I'm sure her father will be delighted to accept you as her husband. But I could not share her hopeful view of the situation.


[User] Please transcribe this text

[Assistant] I gotta put on my remote strap for this one.


[User] Please transcribe this text

[Assistant] I cannot afford to lose a TV. Today is bowling day. I haven't done bowling in years. We are going for the perfect game. Something tells me I'm going to struggle getting a perfect game because I definitely didn't intend for that to happen either.


[User] Please transcribe this text

[Assistant] How does it work with how the ball rolls? Isn't it you turn your remote and it's supposed to make the ball go a certain direction?


[User] Please transcribe this text

[Assistant] I don't understand why it sometimes goes right, sometimes goes left. I don't, I don't know what I do to deserve that. Go like two ticks over here, and then it was always a strike. Okay, that might still be true. Oh my god, I'm on a double, okay.


[User] Please transcribe this text

[Assistant] I might be able to get a perfect game. This is actually a pretty good game. Oh, wait. 171 doesn't seem that good though.


[User] Please transcribe this text

[Assistant] I can never bowl over like 70 in real life though. I am the worst bowler you have ever seen. But we're going for a perfect game here, so. If it's not a strike on our first throw, we gotta restart. Oh wow, I'm actually surprised.


[User] Please transcribe this text

[Assistant] Well that ain't lookin' good. OH HO HO SHIT!


[User] Please transcribe this text

[Assistant] Oh my god, I'm genuinely surprised. I've, I've started off with two strikes in a row. Uh oh. Oh ho ho ho, no!


[User] Please transcribe this text

[Assistant] It was going so good and then all of a sudden the left side of the pins decided to not move. I think the pins got glued down or something. All right. Well, this one's quick. I think this is our... How is that even physically possible? All right. I gotta change up my strategy. So I'm going to go right on the second hash.


[User] Please transcribe this text

[Assistant] It's the same fucking pin every time! They have some glue on the bottom of it.


[User] Please transcribe this text

[Assistant] Well, I guess he brought his buddy along for the ride that time. His, his frickin' opposite side counterpart.


[User] Please transcribe this text

[Assistant] Oh yes, there we go. Back on track. That's our first strike since the first game. Alright, so we were like right there, I believe.


[User] Please transcribe this text

[Assistant] Yeah, we got a turkey! Alright! This is, this is new, new territory for us.


[User] Please transcribe this text

[Assistant] I don't understand it. I-I bold about as good as I do in real life.


[User] Please transcribe this text

[Assistant] Most of y'all probably bowl better in real life than that. Alright, maybe my goal right now should just be figure out how to fucking at least get one strike a game. I got zero strikes that game.


[User] Please transcribe this text

[Assistant] And I'm starting out with almost a strike. I'm trying a new strategy. I'm trying, I'm just, I'm just rolling it down the middle as slowly as I can do it.


[User] Please transcribe this text

[Assistant] Okay, we're gonna have to like, go to one of the practice modes.


[User] Please transcribe this text

[Assistant] Well, I guess that's good enough for me. Oh shit. Oh man. This might be my perfect game.


[User] Please transcribe this text

[Assistant] Okay, now I'm kinda getting, now I'm kinda feeling it. I think I have it down. I think I have it down now.


[User] Please transcribe this text

[Assistant] We still only have one frame that's not a strike.


[User] Please transcribe this text

[Assistant] So this is still a pretty good game so far. Wow.


[User] Please transcribe this text

[Assistant] I'm actually having the game of my life all of a sudden. I actually might.


[User] Please transcribe this text

[Assistant] Alright, we ended with a strike though. I think this is gonna end up being my best game on bowling ever.


[User] Please transcribe this text

[Assistant] In all the years that I've had, that is, that is my new console record, I think. Right?


[User] Please transcribe this text

[Assistant] Oh man, I bowled 234 once.


[User] Please transcribe this text

[Assistant] Oh no. I'm so screwed on this. I-


[User] Please transcribe this text

[Assistant] I HATE this ball sometimes!


[User] Please transcribe this text

[Assistant] My rectum. Why? Why, why, why? Is it always splits now? I can't hit a split to save my life. The ball always likes to roll somewhere else.


[User] Please transcribe this text

[Assistant] 234 was my record! Are you kidding me?!


[User] Please transcribe this text

[Assistant] I'm gonna be one pin short because of that damn split at the end. Okay, I'm on a turkey.


[User] Please transcribe this text

[Assistant] Uuuh, it's just a spare though, so I'm just gonna play through it. NO!


[User] Please transcribe this text

[Assistant] Alright, we got to four. This is our chance to get five in a row. Which I have never done.


[User] Please transcribe this text

[Assistant] I've seen him before on my Wii before. I don't know who that is!


[User] Please transcribe this text

[Assistant] Bowling is all about just doing the same thing over and over again. And for me, it's


[User] Please transcribe this text

[Assistant] Doing the same wrong thing over and over again, I guess.


[User] Please transcribe this text

[Assistant] Why is it when there's only one pin it always likes to go skirt off to the left?


[User] Please transcribe this text

[Assistant] No, that's not looking good. The middle pin, again.


[User] Please transcribe this text

[Assistant] I gotta change my strategy, I'm getting so pissed. I'm just gonna go for the hard frickin-


[User] Please transcribe this text

[Assistant] That middle pin. That middle pin can suck the fattest!


[User] Please transcribe this text

[Assistant] Well, let's see if this remote still works. Aye, it still works. I'm a fucking pussy at breaking Wii remotes too.


[User] Please transcribe this text

[Assistant] What if I turn it left? Maybe I have my directions mixed up somehow. Oh, it still goes left!


[User] Please transcribe this text

[Assistant] Shocker, I guess, I guess there's only one way for the bowling ball to go for me. Turn it right, turn my Wii remote right, turn it left, it's going left. Doesn't matter. It's a one way fucking road.


[User] Please transcribe this text

[Assistant] I want something good to happen. I wanna get a record breaking or a perfect game. Like, I- I'm not stopping. I- I don't know if I wanna say I'm not stopping, BECAUSE I CAN NEVER FUCKING DO IT!


[User] Please transcribe this text

[Assistant] I'm done. I can't. I can't do it. I just cannot do it. It's impossible. This game's rigged.


[User] Please transcribe this text

[Assistant] I don't know what else to say. I've, I've, I wanna keep trying at this, but I, I mean, I have three hours of playing Wii Bowling. I have to tell myself, get a fucking life. This video might be six hours long if I don't stop.


[User] Please transcribe this text

[Assistant] It's in your eyes. The way you look at me with that kind of probing sensitivity, as if


[User] Please transcribe this text

[Assistant] Knowing you'll be hurt, you still have to find the truth.


[User] Please transcribe this text

[Assistant] Smudge. From that charcoal pencil. With which you draw on your sketchbook.


[User] Please transcribe this text

[Assistant] I didn't know Perry had gone into the art field. It's a new one.


[User] Please transcribe this text

[Assistant] Oh, Terry is wonderful. He loves to give free advice to everybody and they all worship him.


[User] Please transcribe this text

[Assistant] Well, people can't help what they feel. Miss Darlington's been in love with you for years.


[User] Please transcribe this text

[Assistant] I agree. Why should either of us live in doubt?


[User] Please transcribe this text

[Assistant] What are you going to do? Mrs. Aswell? Mrs. Aswell?


[User] Please transcribe this text

[Assistant] I'd sleep with him too. You just go up and kiss her. You're birds!


[User] Please transcribe this text

[Assistant] That's, that's private. It was over pretty quick to be honest. The sex at the end was a bit anticlimactic, but the seduction was powerful.


[User] Please transcribe this text

[Assistant] Either there will be like a crazy big war or...


[User] Please transcribe this text

[Assistant] Miracle, uh, deal of the century that Trump wants to do will, uh, make everyone happy.


[User] Please transcribe this text

[Assistant] There's no connection. You have a whole new generation of young people that have no idea what Jerusalem looks like.


[User] Please transcribe this text

[Assistant] They lived all of their life in Beit Lahem or Jericho or Ramallah. They have no, no chance to, to visit Jerusalem when you're desperate and you have nothing to lose. You'll go crazy. But if you're like,


[User] Please transcribe this text

[Assistant] Comfortable and going to the beach and having a nice time. Why bother change?


[User] Please transcribe this text

[Assistant] Either there will be like a crazy big war, or...


[User] Please transcribe this text

[Assistant] Miracle, uh, deal of the century that Trump wants to do will, uh, make everyone happy.


[User] Please transcribe this text

[Assistant] The other part that disagrees with you and find out how to make it better.


[User] Please transcribe this text

[Assistant] I think the right thing is to tell the Palestinian story to someone who don't know, who have no idea, who lives in his comfort zone.


[User] Please transcribe this text

[Assistant] On Tel Aviv or West Jerusalem or in Haifa and, uh, it's shocking to see the reactions of the people. They, it touches them.


[User] Please transcribe this text

[Assistant] We must all go now. Come on, you can tell your people.


[User] Please transcribe this text

[Assistant] So the presentation I'm going to give is a presentation I give to law faculties. It was my job talk paper. It is to law students and people who think about these in legal terms. And the theme of the presentation is that the way that lawyers think about these things aren't


[User] Please transcribe this text

[Assistant] And so a lot of what I'm going to say to you,


[User] Please transcribe this text

[Assistant] It's where I'm not just like repurposing a talk, uh, and, and just, um, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh, eh


[User] Please transcribe this text

[Assistant] Paper, the presentation is Content Moderation as Systems Thinking. Uhm, I'm just gonna chuck up a, a sort of definition for how I'm thinking about content moderation for the rest of this talk.


[User] Please transcribe this text

[Assistant] Uhm, which is their system, platform systems and rules that determine how they treat user-generated content on their services. Now, we could have long debates about this definition, I'm sure. Uhm, yes, it is about how platforms, uh, uh, design their systems. Uhm, and it's also pretty broad. Uhm, and that's gonna be important for the theme of my talk because this is a lot broader than how a lot of


[User] Please transcribe this text

[Assistant] Lawyers, regulators, uh, and, um, and policy makers think about, uh, uh, content moderation.


[User] Please transcribe this text

[Assistant] And the argument that I make in this paper is that there's been this sort of first wave of content moderation institutional design both in platforms and in policy and we need to think about replacing that with a second wave. Okay, but before we get to that argument, let's talk about what the first wave was.


[User] Please transcribe this text

[Assistant] Uhm, okay, so the first wave relies on this standard picture of content moderation, uh, within platforms. And the way that...


[User] Please transcribe this text

[Assistant] People think about content moderation in this framework is that there's sort of an executive, uh, at, at the platform.


[User] Please transcribe this text

[Assistant] And they sit above the rest of the company, uh, one of which, uh, one part of which is the content moderation policy team, the people that sit there and think really hard about speech rules and think, oh, this is where we want to draw the line on hate speech. Uhm, and that policy team is completely separate from the rest of the company.


[User] Please transcribe this text

[Assistant] Including, uh, you know, uh, product growth, engineers, government relations teams, the people that work on ads. Uh, there's just this nice little sort of group of speech philosophers sitting off in the corner thinking about how do we get these things right?


[User] Please transcribe this text

[Assistant] Uhm, and then they sit above this massive bureaucracy, uhm, in the, in the platform, uh, that has a, a whole system of frontline content moderators that are dispersed around the globe, uhm, and that assisted by artificial intelligence tools that make the first, uh, content moderation decision. And if you're not happy with the decision that you get there, you can appeal it up.


[User] Please transcribe this text

[Assistant] And so this is how, uhm, content moderation is thought of in this imagination.


[User] Please transcribe this text

[Assistant] When I give this talk to lawyers, I say this should look really familiar to you, uhm, this kind of picture, uh, because this picture of how content moderation work- works is a pretty good analogue for how we think about, uh, offline.


[User] Please transcribe this text

[Assistant] When we're thinking about how to reform and how to make content moderation quote unquote better, uhm, the key mode of error correction and accountability if you think about content moderation in that form, uhm, is very individualistic. Like what happened in this individual case to an individual piece of content, uh, and it's exposed. What happened?


[User] Please transcribe this text

[Assistant] For lawyers, when you're thinking in those terms, uhm, you think about process being really good. We, lawyers love...


[User] Please transcribe this text

[Assistant] So the problem with this system is we need more due process for users. We need to give them more reasoning, we need to give them more say in the decision, uhm, and the more process the better. The more, sort of like, opportunity to appeal, uhm, and, uh, and things like that, uhm, the better this system is going to work. Now,


[User] Please transcribe this text

[Assistant] The problem with this, and again, this may just be sort of like totally old yawn news to you, but when I present this to people that are thinking about this in policy making terms, this is kind of mind blowing for them, uhm, is that during the time that I'm speaking today, uhm,


[User] Please transcribe this text

[Assistant] Facebook's gonna take down over 700 pieces of con- 700,000 pieces of content, not 700. Even that though would be kinda difficult for-


[User] Please transcribe this text

[Assistant] Uh, lawyers to, to deal with. Uhm, YouTube's gonna take down nearly 200, a quarter of a million pieces of content, TikTok, 35,000 videos, uh, and this Facebook oversight board thing which we're gonna come back to later that sort of operates as this Supreme Court style thing is gonna receive 107 petitions to review, uh, Meta's decisions. Uhm,


[User] Please transcribe this text

[Assistant] That doesn't include a whole bunch of other kinds of decisions. That's only takedowns, right? That doesn't involve...


[User] Please transcribe this text

[Assistant] It's decided around 246 First Amendment cases. Uhm, so it's a pretty different way of thinking.


[User] Please transcribe this text

[Assistant] And the point is that it's a different kind of problem. When you have that kind of scale, you can't use sort of old systems for thinking about these problems. So we need to think about content moderation not as just


[User] Please transcribe this text

[Assistant] Not just as like a bigger version of the problem that we have. The scale shift is so different that we need to think about it completely differently. We need to think about it more as a system.


[User] Please transcribe this text

[Assistant] Which means, instead of thinking about it in these rights-based terms, like here is my speech, and here is the rule, and here is my claim, uhm, we need to think of it much more as a sort of regulatory system, uh, and, and system design. Uhm, so,


[User] Please transcribe this text

[Assistant] The reason why this argument is important is because this sort of judicial-esque model has really been quite dominant in the way that a lot of


[User] Please transcribe this text

[Assistant] The, the policy prescriptions that come up a lot of the time are about


[User] Please transcribe this text

[Assistant] Uhm, adding more human review to individual decisions, providing, having users provide more information, uhm, having more reasoning, uhm, having more appeals, uh, that, that, that can more effectively review individual cases.


[User] Please transcribe this text

[Assistant] Uhm, and this has then flown through to a lot of regulation.


[User] Please transcribe this text

[Assistant] This is a, a bill, uhm, from, uh, this is a bill in Congress, uh, the, the PACT Act is one example.


[User] Please transcribe this text

[Assistant] Uhm, that has, uh, similar kind of, uhm, prescriptions. The PACT Act would require platforms to notify every time, uh, notify a user every time a decision is taken against them, give them an opportunity to appeal and give them reasoning.


[User] Please transcribe this text

[Assistant] Uhm, similarly, there's bills in Texas and Florida, uhm, that have done the same thing. Now those cases, we're gonna come back to those at the end to, uhm, flag them for you. They're gonna, they are gonna head up to the Supreme Court in the next year, uhm, most likely, uhm,


[User] Please transcribe this text

[Assistant] We can talk more about that if that's of interest, but these bills in Texas and Florida, they're currently on ice, so the platforms don't have to comply with them yet, but, uh, potentially could.


[User] Please transcribe this text

[Assistant] They have all of these same prescriptions in them, that the obligation of the platform, uh, in, if they have to comply with these laws, is that every time they remove a piece of content, they have to notify, uhm, provide an appeal, uhm, provide a reason for the determination, every single time.


[User] Please transcribe this text

[Assistant] And that's because this picture of content moderation is what they're thinking about when they're thinking about how to reform it.


[User] Please transcribe this text

[Assistant] Similarly in the European Union, uhm, and this is coming down that this is, uh, uh, passed and going to come into force. This is not a hypothetical. Uhm, the European Union, uh, will require big platforms to provide a statement of reasons every time that they, uhm,


[User] Please transcribe this text

[Assistant] Take a content moderation decision, uh, have an internal complaint handling system, uh, that, uh, that is open for at least six months after a decision is made, uhm, that, that a user can, can use, uh, to, to contest a decision. Six months, uhm, like, if a decision is made about your, you know, uh,


[User] Please transcribe this text

[Assistant] If the, if the platform thinks that your picture of an onion is boobs, uh, and you have to, you have to have a right to, to, to appeal that decision five months later. Like, it, it seems, this is a really, you know, quite burdensome, uh, procedural requirement on, on platforms that might not necessarily, uh, result in, um, significant benefits.


[User] Please transcribe this text

[Assistant] Uhm, and then on top of that, the platform also has to, uh, provide for you to be able to appeal your decision, uh, to an outside third party, independent third party arbiter.


[User] Please transcribe this text

[Assistant] So, really extensive, uhm, really like, what I want to underline to you is this is like really right, that the idea here is that every individual piece of speech is so important that we need to think about it in these really individualistic, uh, terms.


[User] Please transcribe this text

[Assistant] Now I think that this picture, thinking about content moderation in this way, uhm, is blind to...


[User] Please transcribe this text

[Assistant] All of the different ways that content moderation decisions get made that are outside of that bureaucracy, uhm, within a platform.


[User] Please transcribe this text

[Assistant] And, uh, the ex- anti-systemic design choices and trade-offs that ex- affect.


[User] Please transcribe this text

[Assistant] Uh, speech, how speech moves through the platforms. I, it's blind to a lot of the thinking that all of you in this room spend a lot of time doing about how do we design this system, uh, to be, to be healthier, uhm, in the, in the first place, rather than fix up the mistakes at the end. Uhm, and so, you know, in terms of heterogeneity of institutions involved in content moderation,


[User] Please transcribe this text

[Assistant] This part of content moderation is just a very small part of how platforms think about...


[User] Please transcribe this text

[Assistant] So even within a single platform, you have behavioral content moderation that falls outside that picture. So you have like,


[User] Please transcribe this text

[Assistant] Groups looking for influence operations that are not focused on individual pieces of content, but are focused on how a group behaves as a whole. So you're not going to see those kinds of patterns if you're only looking at individual pieces.


[User] Please transcribe this text

[Assistant] You have government relations teams that have their own perspectives on how speech decisions should get made. And you have product design teams, the people that are deciding where to put the like button and the share tools and all of the interventions that might affect how speech moves through a platform that aren't those individual decisions about a piece of content.


[User] Please transcribe this text

[Assistant] And then you also have, uh, people outside the platform making decisions too. So uhm, we're increasingly seeing


[User] Please transcribe this text

[Assistant] Content moderation decisions delegated to outside third parties. So for example, fact checking organizations or, uh, authoritative institutions, you know, some of the most prominent being, for example, the World Health Organization policy was imported into, um, platforms content moderation decisions, uh, during the pandemic. Um, we also have, uh,


[User] Please transcribe this text

[Assistant] Users increasingly involved and empowered in content moderation decisions who don't, their decisions aren't really seen in the bureaucracy and so much of the regulation that we see coming down the pipe for thinking about how to regulate content moderations and platforms are blind to


[User] Please transcribe this text

[Assistant] Community-based moderation. The mismatch between those two is really, uhm, is really obvious.


[User] Please transcribe this text

[Assistant] Uhm, we also have governments who interact with platforms in different ways, both formally and informally, to affect content moderation. And increasingly we have, uhm,


[User] Please transcribe this text

[Assistant] Other platforms, so we're increasingly seeing much more collaboration across the industry, uh, both big and small in terms of technology sharing, uh, database sharing, uhm, eh, eh, information sharing that, uhm, that means that


[User] Please transcribe this text

[Assistant] You know, if you are looking downstream at the end decision and you're only thinking about that decision, you're gonna miss where exactly the actor.


[User] Please transcribe this text

[Assistant] And I'm not going to spend too long on this because you all know this far better than I, but if you're thinking only about that bureaucracy and those kinds of individual rule and individual piece of content, you're missing all of the systemic design choices that are involved, uhm, in, in, uh, in


[User] Please transcribe this text

[Assistant] 700,000 decisions correctly every hour.


[User] Please transcribe this text

[Assistant] And so you have to, you know, whether it's the, uhm, the, the confidence threshold on the, the tools that you use, uhm, or, or whatever it is, uhm, whether you change the way that you think about writing your rules so that it's blunter and maybe overbroad.


[User] Please transcribe this text

[Assistant] Uhm, because you want to apply a simple rule in a large amount of cases, uhm, you have to make these choices about false positives and false negatives in a way that we don't really think about in like a First Amendment context or an international human rights law context where we're thinking really particularly about those individual facts where a court would like to look at an individual speech act.


[User] Please transcribe this text

[Assistant] For eight months and then come out to an outcome. Uhm, that's, it's not really that same, uh, uh,


[User] Please transcribe this text

[Assistant] Problem of, uhm, of error, error tolerance.


[User] Please transcribe this text

[Assistant] Uhm, we've already talked about how platforms have to think about, you know, how much of the rules are they going to enforce and how much of it are they going to leave for their users to enforce, uh, and, and, and moderate themselves, uhm, in a way that is a system design choice that isn't, uh, that isn't visible if you're only looking at that, uh, bureaucracy. Uhm,


[User] Please transcribe this text

[Assistant] Super curious for some of your thoughts about this. Uhm, I have been deeply skeptical in my work about the


[User] Please transcribe this text

[Assistant] Overall benefits of always providing users for like procedural justice in such extensive ways in every case because


[User] Please transcribe this text

[Assistant] If you are, you know, loading on lots and lots of, uh, uh, procedure and due process.


[User] Please transcribe this text

[Assistant] There are costs to that. There are costs to, uhm, efficiency, getting things done quickly, uhm, which can be really important. There are costs to, uhm, and we can talk about more, more about this in, in a minute, but there's, there's not necessarily evidence, or there isn't always evidence that decisions are


[User] Please transcribe this text

[Assistant] And so there's these real trade-offs as well that get made in thinking about how to design a system, uh, that I, that I'm not confident that this, like,


[User] Please transcribe this text

[Assistant] Piling on all these extra procedures is necessarily, uhm, uh,


[User] Please transcribe this text

[Assistant] Brazil and the U.S. and Myanmar and Germany. Uhm, you have a choice to make about, do you have a rule that you apply


[User] Please transcribe this text

[Assistant] How do, how does, how does justice play into those kinds of choices when you're operating at the scale, uhm, that these platforms operate?


[User] Please transcribe this text

[Assistant] As you know, I should have said at the beginning, but as a parent now, really thinking about the big platforms, uhm, that.


[User] Please transcribe this text

[Assistant] This appeal system, this individual expose review system isn't, uhm,


[User] Please transcribe this text

[Assistant] Of a, of an error choice made further upstream.


[User] Please transcribe this text

[Assistant] On the other hand, it could be evidence of systemic bias within the platform, um, that, that is, that is, uh, carried, carried out. And if you are just reviewing that individual decision, you're not going to see those systemic issues, um, nor are you going to fix them, um, because if you just sort of fix, uh,


[User] Please transcribe this text

[Assistant] That one time that, uh, that, uh, as we know, for example, uhm, uh,


[User] Please transcribe this text

[Assistant] Black users suffer disproportionate harm under, like, hate speech, uh, uh, algorithms that, or, you know, there's been research showing, uh, hate speech algorithms that many of these major platforms use. If you just fix them one by one, you are not gonna be fixing the upstream problem, uh, of system design that's, that's fix, uh, that's causing that problem. Uhm.


[User] Please transcribe this text

[Assistant] It's resulting in, this picture of content moderation is also resulting in mandates that are going to be transparency theater, where it's requiring platforms to produce a whole bunch of reports about how many aggregate decisions did you make in this particular, uh, uh, period, um, that


[User] Please transcribe this text

[Assistant] Is what platforms already do and actually reveal very little about what's going on on the platform. They're great for producing slides for slideshows that tell you how many pieces of content a platform will take down in an hour, but I don't know how much you say unnecessarily for doing anything deeper than that.


[User] Please transcribe this text

[Assistant] Uhm, and we've already talked about, uh, the, the process theater that some of these mandates, uhm, can, can result in. Uhm, where, uh,


[User] Please transcribe this text

[Assistant] Users get more process, uhm, but don't necessarily...


[User] Please transcribe this text

[Assistant] Go through this really quickly, uhm, the transparency theatre is, you know, what I was saying that because it's focusing on these, uh, decisions in the bureaucracy, a lot of the transparency mandates that


[User] Please transcribe this text

[Assistant] Lawmakers in, in Congress, in the states, in, in EU are requiring is like figures about, you know, how many pieces of content did you take down?


[User] Please transcribe this text

[Assistant] You know, these, these reports are relatively uninformative. They don't tell you the accuracy of the figures that, that are presented. They don't tell you if a platform is taking down, you know, 50% more of that kind of content in a period. Is that because they changed their?


[User] Please transcribe this text

[Assistant] Algorithm, is it because users posted more hate speech? Is it, you know, what, what is the, what is the reason for that increase? And that, that is not a clear from the, the uhm,


[User] Please transcribe this text

[Assistant] Uh, the, the face of the report, and it also might lead to content moderation inflation, where, like, there is an incentive at the moment, I think, for platforms to say, we are taking down ever more pieces of content, uh, look how good a job we're doing, we caught one billion spam accounts, uhm, and that might have perverse incentives on how, uh, platforms think about content moderation. Uhm, okay, uh, process theater. Talked about this briefly, uhm,


[User] Please transcribe this text

[Assistant] About the, the, the costs of, uh, of, uh, doing all of that extra process.


[User] Please transcribe this text

[Assistant] I do want to be clear that I don't think it's not, it's not the fact that I think that all process should be, should be gotten rid of. I think it just needs to be thought about much more carefully about what is the goal that you're trying to achieve with extra process and are you actually going to be achieving that outcome, which is not what these regulations are, uhm, are.


[User] Please transcribe this text

[Assistant] So it may be very well the case that like in hate speech, for example, the kinds of errors that are made, uhm, are the kinds that will be, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh, uh,


[User] Please transcribe this text

[Assistant] Much more, uhm, easily identified or better identified and fixed through, uh,


[User] Please transcribe this text

[Assistant] That might not necessarily be true for adult nudity for example, where the appeals process might be much less useful.


[User] Please transcribe this text

[Assistant] In terms of, uh, individuals that use appeals are not necessarily representative, uhm, so this is, uh, information from, uhm, the meta oversight board's transparency report. Uhm, and,


[User] Please transcribe this text

[Assistant] So it can hear appeals from users who are unsatisfied with their, uh, with their decision taken on, on, on any of Meta's platform.


[User] Please transcribe this text

[Assistant] And those top lines, the pink line, uhm, is the number of times users appealed if they had their content taken down and they wanted it to be put back up.


[User] Please transcribe this text

[Assistant] And that bottom line, you can't, you almost can't see it because it's so close to the zero line, is the number of times people appealed, uhm, when they saw a piece of content that they thought should, broke the rules, and they thought it should be taken down.


[User] Please transcribe this text

[Assistant] That we necessarily know of to think that meta is much, much better, um, at identifying, uh, takedowns than it is, that it's, uh, taking down content. You know what I mean? There's no reason to think.


[User] Please transcribe this text

[Assistant] Uhm, the meta is getting one decision right much more often than it is getting the other decision right. Why might we see that disparity? Well, because the user whose content is taken down is like much more invested. And they have to like appeal three times and they're like, okay, I got the wrong decision. I'm going to appeal. I got the wrong decision. I'm going to appeal again. And then I've got the wrong, and they're just going to keep appealing because they really want their post back on the platform.


[User] Please transcribe this text

[Assistant] Someone who sees someone else's post and they think, well, it breaks the rules, then they kinda have to go through the process again and maybe provide more reasoning. At some point, they're probably gonna go, eh.


[User] Please transcribe this text

[Assistant] So the, the argument that I make in the paper is that we need to stop thinking about this so much from an ex post fixing things behind, uh, after the fact to thinking much more about system design choices, uh, which, uh, I...


[User] Please transcribe this text

[Assistant] Hope many of you in this room are in favor of, and instead of thinking it in individualistic terms, thinking about it much more in terms of systems, uhm, and going from rights-based thinking to much more regulatory thinking.


[User] Please transcribe this text

[Assistant] Uhm, which is brings us to that argument that I was making on the first slide about moving from this first wave of thinking about regulation to moving to a second wave. Uhm, that looks much more


[User] Please transcribe this text

[Assistant] In two different buckets of reforms than the kind of imposing all of these procedural appeals mechanisms, which may not be applicable for every kind of platform. Thinking much more about structural measures and more systemic procedural measures rather than individualistic. So structurally,


[User] Please transcribe this text

[Assistant] I'm interested in thinking about separation of functions, uhm, what I mean by, well I'll come back to what I mean by that. Complaints mechanisms, uhm, disclosures of third party decision making and retention of and provision for access to data.


[User] Please transcribe this text

[Assistant] Separation of functions. What I mean by this, uhm, is in part, uh, I mean, one of the biggest concerns that we have in platform content moderation, decision making and system design is that it, it is not


[User] Please transcribe this text

[Assistant] They're biases, uhm, in the way that the system is designed or the decision makers. And we have, for example, evidence to believe that, uh, people who are involved in, like, government relations, uhm, come in and step in after the fact into, uh, content moderation decisions, uhm, and change them even though, uh, the, the decision was taken in accordance with the platform's rule.


[User] Please transcribe this text

[Assistant] So uhm, I think separation of functions eliminates the incentives, uh,


[User] Please transcribe this text

[Assistant] I think a complaints mechanism for whistle-blowing, ahhh.


[User] Please transcribe this text

[Assistant] To the extent that outside contacts are involved in decision making, uhm, I think that eh, and this is in particular as a free speech scholar, uhm, thinking about how governments, uhm, influence decisions, uhm, I think is a, is a, one of the things that I would really like to see.


[User] Please transcribe this text

[Assistant] Retention of and provision of access to data is not, I think, uh,


[User] Please transcribe this text

[Assistant] I'm not going to spend too, too long on this.


[User] Please transcribe this text

[Assistant] Kind of content moderation, uh, governance and system that I'm much more interested in is forward looking and thinking about risk assessments, thinking about what are the possible failure, uh, uh, modes for, yes.


[User] Please transcribe this text

[Assistant] Because you're right, there is an inconsistency in what I'm saying, uh, and so it's good to clarify. You know, on one hand I'm saying we need to think of this much more as a system and all of the product design choices are influencing how we think about content moderation. On the other hand I'm saying to the extent that there are content moderation decisions made, I want them to be hived off from people that have, uh,


[User] Please transcribe this text

[Assistant] So what I mean by that, this is a, uhm, is that once content moderation decisions are made, they should not be interfered with other parts of the company. So for example, I think it is relevant to have


[User] Please transcribe this text

[Assistant] A way of ensuring that decisions are both unbiased and also perceived to be unbiased, uhm, is by ensuring that...


[User] Please transcribe this text

[Assistant] They can't be interfered with after the fact by people outside that.


[User] Please transcribe this text

[Assistant] And it's important because I'm not saying, let's abolish the appeal system. Maybe some of what I've been saying might have sounded like that, which is not what I'm saying. What I'm saying is that we need to design it in a way that is much more attentive to when is it beneficial and when is it not beneficial. And that is not the way that these systems are currently being designed, which is mostly, I think, performative.


[User] Please transcribe this text

[Assistant] Thinking about this, much more interested in accepting that there is going to be system failure and trying to identify where the system's failures are on an ongoing basis through auditing and quality assurance measures and thinking about things much more, uh, at a big picture level with continuous improvement rather than focusing on individual mistakes. Happy to talk about that more.


[User] Please transcribe this text

[Assistant] The one thing that I, you know, I wonder is that