more of you here than I expected, given that J. Ruby is happening at the same time, so welcome. My name is Liz Abanate. I am an engineer at New Relic up in the land of Portland. A little colder than here and a lot weirder than here. So I'm going to talk to you today about programming, education, and the American Dream. And everybody that I've told about this talk has told me, that seems really nebulous, Liz. That's kind of a big topic, Liz. What are you going to really talk about, Liz? Come on, Liz. And I got really tired of hearing my name. So I made an outline for you, because I'm an academic at heart. We're going to go over a few different things today, the learned code movement, education in America, what the American Dream actually is, and then how this all comes together when we educate the programmers that we work with today. So I'm going to kick it off with a little overview of the learned code movement. How does this all get started? It's a little difficult to give a straightforward explanation. As with anything in our history, things are more complicated than just one event. It's never just one thing. But I do want to focus on a few different things, these four specific things. And while there's more than four, these are big factors. These are things that we all deal with on a day-to-day in our work lives. I'm going to start with startup success stories, the availability of the new curriculum, what it means to have a lower barrier to entry for these new technologies, and the end result of Obama telling us all to do an hour of code. So while the stock market doesn't always love software as a service companies or social media companies like Twitter, people love them. We love them. We work on them. This is what we do for a living. And we all love a good story about a programmer or just nobody who comes up with nothing, makes a company and is super successful. We love it so much that Facebook got its own movie. Facebook became part of American drama because we made it into a movie. Like that's how excited people are by these startup success stories. They're so excited they'll go pay $13 to go see a matinee in New York City of this movie about Facebook. Makes sense, right? And I get why they're excited. Mark Zuckerberg is super rich. He's worth $33 billion. He was ranked number 22 on the Forbes 400 list just last month. And this year Forbes did something new with their list. They added this self-made score down here. His wealth is self-made. Well, what exactly does that mean? So Forbes decided it was kind of silly to put Silver Spooners, a level one on the self-made score, people who grow up with a lot of money and do nothing to make that money grow, on the same scale as someone, as say, Oprah. And so they invented this scale just this year. And they gave Zuckerberg an eight. So for some perspective on that, one means born wealthy. Ten means unbelievable life hardships. Zuckerberg is an eight because he came from a middle upper class family and had no real help bootstrapping himself other than, you know, lots of ECs. Oprah is a ten. Her life was only two points harder than Mark Zuckerberg, according to Forbes. According to Forbes. So there's that. But they also did a look back at the old data, and they found that in 1984, less than half of the list was self-made. And just so we're all on the same page, self-made is a four or higher. Not like a five or higher where you think it would be and like you're right in the middle, but it's like a four or higher because three is wealthy and actively working to improve your wealth. So now in 2014, almost three quarters of the list are self-made. There are at least a four or higher. We really like what happens when we make our own fortunes. I mean, it would be easier if we just had it, but you know. Another big factor is the availability of curriculum. It's just plain easier to learn something if there are more ways to learn it because not everybody has the same learning style. In-person courses started popping up that were a little bit more affordable than say going to MIT or Harvard or even a University of California school like I went to, not for CS. And so these classes ranged from one-day workshops, non-profits like Girl Development offered affordable workshops across the country in major cities, and then development courses started popping up.  But regardless of the types of programs, the fact that these in-person courses were available outside of a four-year CS program made it a lot easier for people to learn new things. But where a lot of people, myself included, get started these days, I sound like an old person, is through free and affordable online curriculum. These interactive video tutorials or where you talk to the computer and it talks back to you. Those things are oftentimes the first 4A into coding. It's your introduction to Ruby. It's your introduction to JavaScript. It's how you learn HTML, the very, very basics. And these online resources are amazing because they can teach you those foundational principles without you having to drop a couple thousand dollars on a university course or an intensive program. And that makes it a lot more accessible to people who don't have the wealth to afford expensive programs or who don't have the time in their day or can't afford to quit a job to quit a full-time program. And after that, in the past year or so, I've been seeing a lot of these. We now have online curriculum that includes mentorship. So you get one one kind of office hours, almost like you would if you were a student at a university.  You would get to learn at a one-on-one level. So if you're having a really, really hard time understanding recursion, you can sit down and talk to somebody about it for a couple hours on your schedule. This means if you live in North Dakota, you can get the same quality of education in theory as someone who lives in San Francisco and has a lot more options available to them. And this makes it much easier for people who work jobs with odd hours, can't afford to quit a job or can only do part-time commitments to learn these skills. And so we have all these different options and a big factor here in this curriculum being so popular is that now there's a much lower barrier to learn these new technologies. We started with these cloud development environments, which as professional developers, not a ton of us would work in on a day-to-day, but they alleviate cost and complication. This computer, my wonderful work computer, I never could have bought when I was in college. I could not afford this. But say a Chromebook or an old PC that I got second hand for free from a friend, I could have done cloud development and I could have learned on a cloud. It's also helping to lower the number of things you need to learn right out of the gate. You don't have to manage a Dev environment. You don't have to deal with conflicting versions of RubyGems. It's all just there. It's all set up for you. And while it's super important to learn maintaining an environment and maintaining different versions of things eventually in your career, it's sometimes a barrier. It's sometimes a blocker. It's really frustrating to not be able to write code because you can't figure out why your server won't start. What's more important? They're both important, but learning one at a time is more effective. And the cheaper equipment like Chromebooks really makes those cloud development environments useful to people. The Y Web Academy in Madison, Wisconsin is a free program for underprivileged youth and for people of color to learn full stack Rails and JS development. And they're doing it on $300 Chromebooks. It's a nine month program. And they're learning everything that they can learn on that Chromebook and cloud development environment. That's awesome. $300 as opposed to $2,000 or so. That's pretty cool. We're also learning simpler languages. And I say simple because it's all relative. But Ruby and Python don't have a lot of that syntax. And when I teach JavaScript to beginners, they don't have trouble understanding the concept of, say, conditionals. But that syntax is a bear. They cannot figure it out. And you give them a linter and they're like, but I don't understand. Where does it go? And they get so confused and so frustrated. Syntax should not be a blocker to learning. And while syntax is an important part of programming languages, while it is the thing that calms my soul when these parentheses all match up and the curly braces are all there and I love it, it makes me so happy and I find joy in that, it was a huge blocker for me when I started learning because I couldn't figure out where the damn parentheses go. And you don't remember what order they go in. That's a blocker that we can get rid of now if you learn Ruby or Python. And then move on to new language when you've mastered these concepts. We've also given people more options of things to learn with this emphasis on user experience and responsive web and mobile apps. People can now learn what they're interested in instead of the thing that has jobs. You don't have to learn about DevOps and securities and servers if it's not your interest. You're more than welcome to if that's what you're interested in. But sometimes that stuff is really hard for a beginner. Maybe learning how to do responsive CSS is really your jam. And there are a shitload of people out there who would love to hire you so they don't have to write that CSS. Your job is not just writing code. Your job is building user experiences. And that's just as technical as running the server. So these opportunities are newer. The last I'd say five, maybe eight years. And that makes it easier for people to get into the field and find where they belong. And lastly, it's a small thing, but it matters. In December of last year, Obama gave a speech about jobs in the U.S. And one thing he said was that youth should learn how to program their phone instead of just playing with it. And he encouraged everyone to do an hour of code a day for a week in December. It was this big challenge that he issued. And a ton of people did it. People did it in classrooms with pen and paper. They did it on computers. They did it at work. They did it at home. I even got an email from my CEO at my last job. This is, I did my hour of code. Did you? It's like, yes, I did. I actually did my eight hours of code today. But it still changed the way a lot of people approached it because they had the leader of their country tell them that it was something that they could do. And that's not something that everyone had initially thought of. But now that we're telling you, anyone can learn to code, you can program your phone. We're starting to discover that there's a little bit of a discrepancy between this type of marketing and the actual lived reality. And I do have to pause for two seconds in this talk to tell you a little bit about myself because it does inform a lot of what I say about this topic. I was going to be a high school teacher because there's nothing more exciting than teaching high school students, right? They are soulless monsters. But I was convinced that I could be the teacher that would make them love to read and like be super interested in history. But I didn't. I went to a code bootcamp and instead, I'm not really sure if it's an instead because they still teach people programming.  I was at a dead end startup and I hated my job and I wanted something more challenging. I knew HTML and CSS and I was like it can't be harder than CSS, right? It's not. I'm going to say CSS is easier. So I learned my first programming language in 2013 and because I didn't come from a technical background it was one of the hardest things I've ever done.  It was my dad's work computer. I still don't know to this day what type of work he accomplished on this computer because he worked in the field for Pacific Gas and Electric here in California like climbing poles. Why did he have a computer at home? But I still learned to type at a really young age and that was my exposure to tech prior to probably late high school. When I started inspecting the code on my favorite in sync fan pages so that I could change the colors of the fonts to make them more readable. And that was how I learned HTML and CSS. I really tried to find my in sync fan pages that I made when I was like 12 but I couldn't find it. Web archive let me down. But I never saw tech as a career. I just didn't because my parents were working class. My mom didn't work because she couldn't afford to work and send both of us to daycare. And my dad worked in the field.  And my friends parents were teachers or social workers or they worked for the garbage collection service like everybody I knew was working class everybody I knew didn't work in this field. It just wasn't where they were. And even when I got to college and used email my comprehension of email was I hit the send button. And then there's someone that grabs my email and like walks it over to that person and gives it to them like it's a manual process behind the scenes. And I didn't understand how tech worked even when I was 19 and 20 and was using it every day to do research to write papers to send emails. It just wasn't my thing. And then I started working at a startup and that was where my eyes got opened and I realized the internet was not powered by Excel spreadsheets. I did not know what databases were. I found out very quickly. And then I decided to learn how to program. But really I decided to learn how to code because that's what everybody told me to do. Everybody said learn how to code. Anyone can learn how to code. Here's how you do it. Here are the different ways to do it. Here are all my opinions on things run with it. But it turns out that learning to code is not the same as learning to programming to program. And those are two very different things. Nobody really tells you this when you're typing away on Code Academy and you're writing your first Ruby method. Nobody tells you that code and programming are two different things. And so really it's like learning to read. You can learn to read as pattern recognition or you can learn to read as a rich understanding and deep analysis and comprehension of your subject matter. And so if you look at it from that perspective, writing code is really nothing more than typing from a prescribed set of syntax or prescribed set of rules. So we're all just really overpaid typists. That have very specific rules we follow when we type. And that's what it is if your job is code. But if your job is programming, it's not just typing. It's more than typing. And just like becoming a good writer or a good literary analyst comes from learning from others and gaining strength from what they've done and what they haven't done. So it is being a good coder. We learn a lot when we work with other people. And this learn to code movement while wonderful and we're all learning these new things. Sometimes we're learning them from people who are not prepared to teach us. They're either not prepared because they don't have the right curriculum or they're not prepared because they don't have the right training. So you end up having a very frustrating experience for students. And what could start as learning to program actually just becomes learning to type code. And when you're learning from these unprepared instructors or if you're having a bad learning experience, it's not just clicking for you.     But you can't figure out how to do it. And they keep telling you it's easy. It's easy. It's easy. And they're saying it's easy. I did it. So can you. But as a learner as a new person coming to programming, what it actually sounds like to me when I try to do what you tell me is easy, is that it's easy and I did it. If you can't, it's your fault.  And this is not a really rewarding experience for students, whether they be adults learning to code or kids in elementary school. And phrases like this are really common, not just in education of developers, but in school itself. You're teaching kids algebra and they're really stumped by it. Teachers will say it's easy, just do this. But those phrases put the responsibility for learning among the student. They take the responsibility away from the institution, away from the instructor. They're no longer responsible for the quality of their education, the quality of their instruction. You're responsible for it. You're responsible for absorbing the way they're teaching you, regardless of whether or not it works for you.  You don't learn a lot and you don't learn well. Because different students do not excel or fail because of innate abilities. Yeah, sure, I'm sure we've all read studies that say people born with this type of brain are better at this type of thing. But at the end of the day, you're better at something because of your exposure to it or because you were raised with it. If you have a great education and you're exposed to computers from age two and up and you can type faster than I can at age two, it's likely that you're going to work with computers and you're going to be really damn good at it. Not everybody has those type of exposures when they're young. Because of the way we teach kids. So shifting focus a little, we're going to talk about educating youth in America, not with coding, but in regards to just education in general. And there's a lot of different ways that we teach kids, but in general, it's focused on achievement and lectures. And in the recent past from kindergarten on through high school, we did this system called leveling. And it was kind of crappy because instead of being challenged to learn new skills, you were placed at your level. And if you're at your grade level, that's cool because you're learning new stuff. But if you're a student in the fifth grade and you read at a third grade reading level, for your entire fifth grade year, you will continue to read at that third grade reading level. And when you go to the sixth grade, maybe they'll bump you up to the fourth grade reading level if you pass the test. And this was leaving a ton of students behind. A really good example of this was in a junior high English book or English history book, they were learning about the founders of the United States. And there's a sentence in this book, very simple sentence. Ben Franklin was one of the founders of our nation. And in the at grade level book, that was what it said. Ben Franklin is one of the founders of our nation. In the low reading level book, it said Ben Franklin started this country because apparently founder and nation are too complicated. And so when you're not getting a new vocabulary, when you're not being challenged because you're already under performing, you're not learning. And so this was the state of education in the US until a few years ago when they introduced common core standards. And I'm sure if some of your parents, you may have heard of common core and you may have seen those like math problems that are ridiculous, like I have $5 and Jimmy has three figs. How many mice can I buy? Like that's kind of what it's like. But what common core really is, is a set of standards. They were created so that students could go from kindergarten to high school and acquire the skills that they needed for their career or for college or for life. No matter what school district they're in. And they were created with international educational practices in mind because the US school system is really bad. So we looked at countries that had better school systems than we did. And we developed these standards using research and evidence and we made them clear and we made them understandable. There's lovely outlines and there's codes for every single skill like CCR 430 describes a reading skill. Cool. And these standards have been adopted in 43 different states and the wash and Washington DC. But the problem is we made these standards and once again we did not prepare our teachers. We did not train them. We did not teach our teachers how to teach our kids. And so parents are pissed because their kids come home with homework about figs and dollars and mice and they don't know how to help them solve it and the teachers don't know how to teach it. So really when there was this article in the New York Times about common core that was published recently it was written by somebody who helped form the mathematical standards for the common core that get the most flak. He said in the hands of unprepared teachers the reforms turned to nonsense perplexing students more than helping them. So we're back to where we were before. We're back to students aren't really learning the thing they're supposed to be learning and students aren't really being challenged because how can you be challenged if your teacher can't teach you. And there's still leveling students within common core. There are still levels. You can still be below your grade level which is kind of not the point really. And so common core isn't the solution because it reinforces the checkbox learning. If you can do score or if you can do skills CCR 430. You've mastered that reading skill and now we can move on and teach you more stuff. Common core doesn't revisit skills. It doesn't emphasize skills. It doesn't encourage you to keep working with those skills. It just drops it in there. Can you check the box. Yes. No. Cool move on. Can't check the box. Doesn't matter your screwed. Next grade level. That's common core. And it doesn't address the disparity in educational quality or dropout rates in the nation's poor schools. It just kind of says they're there. Which is not really a good way of fixing things. But when we talk about student demographics and we talk about teaching students we talk about poor schools and good schools and like what does this all really mean. So I dug up some numbers on the top five stem high schools in the U.S. Turns out it's super easy to find information about the best schools in the country. U.S. News ranks schools every single year for high school and college based on their standards. And so I only chose the top five stem high schools because I wanted a relatively small sample size to work with because the numbers get kind of scary after a while. So it's about fifty seven hundred students a year that they educate. And that's not the students that graduate that students that walk into the school every year. About I'd say a quarter of those given that they're four grades graduate every year. So if you look at the minorities it's actually better. It's actually more representative. Then what the U.S. has the U.S. has about fifty percent of the population is part of a minority group. According to last census. So this is pretty good. This is pretty cool. Sixty three percent of the students at the top five the very best stem high schools in the U.S. are a minority. That's pretty awesome. That's really good.  There are twelve Native American students at these schools. All all of them all five total twelve fifteen Hawaiian or Pacific Islander kids. And so. Are you really. Educating the representative minority group in the United States. Not really. And schools here for this statistic they report what percentage of their student body falls into these racial ethnic group categories. And when I did the math. A lot of the schools had point six students. That were Native American or Hawaiian or black or Hispanic or whatever minority I was looking at. Point six. Students like. From your neck down. Like a neck down student or like I've waist up student like whatever sixty percent of your body masses which is. I mean it doesn't really matter if it's point six or one student it still sucks to be the only one. And.  Of the fifty seven hundred students. At these schools less than a percent less than one percent a single percent. Are impoverished. And. That's really sad but like how many kids is that actually. That many. So like this section of the room that's you guys I guess. Thirty seven kids and this is the national number. But it's actually only the number for one school because only one school reported having any impoverished kids at their school. So really it's one school is working to you know correct some socioeconomic disparity in the U.S. while the other four. Don't serve socioeconomically disadvantaged students. And so I decided I wanted to do some comparisons to the worst schools in the U.S. and see how they stack up to the best high schools. There are no lists of the worst schools in the U.S. they don't exist. I Google best high schools in the U.S. I look in education statistics for the Department of Education I look at state statistics can't find it can't find bad schools they don't exist. And I kind of understand this because no parent wants to know that their kid is going to the worst high school in the U.S. you don't want to be like I'm sorry a little kid you're going to one of the worst schools. And tears like you don't want to tell that to your kid. And the only list that I could find was actually created by a neighborhood researching company called Neighborhood Scout which based on the information on the page I can only imagine is created to help white people avoid poor neighborhoods. So I didn't trust the data so I didn't use it. So I have no statistics on the worst schools in the U.S. because nobody will tell me what the worst schools in the U.S. are. So instead I decided to look at the nationwide literacy rates. I started with fourth graders. And these are fourth graders who take the national reading test. This is across all states in the U.S. this is every fourth grader. At or below basic a third of students basic depending on the state either means at your grade level or one level below. So you should be reading at a third or fourth grade level to be in the 66 percent. Or yes sorry that was confusing. And so a third of our kids cannot read at the grade level in the fourth grade. About half of black Hispanic Native American and impoverished students read below basic. So they're like reading at first and second grade level in the fourth grade. When we test them to get an eighth grade it doesn't improve that much. Only seven percent of the students that were reading at or below basic can bump up in those four years. Four years and seven percent of students in the U.S. can't improve their reading enough to read at the grade level. It doesn't get better for minorities and impoverished students either they're still hovering right around the thirty nine to forty ish percentile. It was pretty bad. It's not fair. It's really crappy. And so now that we're looking at this picture of education in the U.S. why are we pushing computer science. Why are we being told that anyone can learn to code. When we have fourth graders and eighth graders and high school students who can't read at their grade level. They're not meeting the standards for reading and math. Why are we trying to teach them another subject when they haven't mastered the basic subjects. This is a this is a terrible idea. Oh you're doing really bad at paying your mortgage. How about you buy a new car. That's that philosophy. That's a terrible philosophy. And how do we expect our students to achieve anything or to even dream that they can achieve anything. If when they graduate high school. They can't read the great Gatsby or they can't solve an algebraic equation that the state expects them to that the state says declares their competent after graduating high school. How can they expect themselves to do well in life. If the state is telling them that they are failure and we're not doing anything to help them. And so. Transitioning kind of quickly into the American dream which is kind of the big overall thing about this talk. I'm going to talk really quickly about what it is. I'm going to share with you a super scientific study I did. So the American dream no surprise was coined right at the beginning of the Great Depression. It was coined by an author named well it doesn't really matter his last name is Adams. He wrote a book and a bunch of essays about it. And he was so proud of this thing that he came up with. That he said that it was the most important thing that we've ever done. It was our unique our being the United States. Our unique contribution to the civilization of the world. Like so pompous but also really really true. And so I asked some people when I was you know proposing this talk and researching this talk what do you think the American dream is. And this is super scientific I only ask my friends I only ask people that I know. So these are some of the responses I got. White picket fences owning your own house. Striking it rich and becoming a millionaire. Being an entrepreneur or being your own boss. And no this one was from my working class mom. Whereas being an entrepreneur was from a software developer. So another person said nuclear families and not worrying about money. Someone who moved here from Norway recently said coming to America with nothing and becoming rich because of your own will to succeed. My husband said Elon Musk. I mean he's right though. And then last person I asked that I'm not sure really I'm just I'm not sure. But it seems like a lot of rich white men now and they don't want to share it. And all of these people are right they're all components of the American dream but the thing that I really want to focus on is what Adams had to say about it in this one paragraph. And it's a lot of text I'm going to read to you because I don't want to have to force you to read. The dream is a vision of a better deeper richer life for every individual regardless of the position anxiety which he or she may occupy by the accident of birth. Accident you were accidentally born into this sad position or this great position don't worry about it. It has been a dream of a chance a chance to rise in the economic scale but quite as much or more than that. Of a chance to develop our capacities to the full unhampered by unjust restrictions of caste or custom. Or if you're that really rich developer in Silicon Valley splitting up California into six different states so Silicon Valley can have different legislation. So this last part is really the part I want to focus on the chance to develop your capacities to the full without any restrictions. And that's really kind of when people talk about the American dream striking at rich because of something that they did on their own. That's what they're focusing on. It's about that individual achievement and unlocking your own potential. Who cares about the barriers. And this is startups. This is what it means to be innovative. And this is what it means to be a hashtag thought leader. Oh no. Never leave your calendar open. Apparently I'll warn you about meetings that you're not at because you're in San Diego. And so this is what it means to disrupt. This is what it means to be Uber. This is what it means to be any startup company that is trying to change things. They don't care that it's unethical. It's a threatened journalist. Barriers be damned. They're going to be successful no matter what. That's how the American dream has been realized by startups. And it's starting to impact how we educate programmers. And maybe starting might not be the best word there because it's been going on for a few years. And so we're talking about whether or not anyone can really learn to code. But unfortunately, we are leaving people out of this when we say that anyone can learn to code. When you think about brand new computers, and I don't mean brand new and that I just got a shiny MacBook Pro. I mean when computers first became a thing you could put in your own home and the internet was a thing that was brand new, there wasn't a computer science major. You couldn't go a major in computer science at most of the universities in the US. People taught themselves or they had a friend teach them. They learned from someone else. And this is what makes our industry so amazing. The ability as an engineer to learn a new programming language because you taught yourself. You found the resources and you taught yourself. We're autodectic learners. We really like to absorb new knowledge. We really like to try new things. And this is so important in our industry that we're actually amazed. People that we admire become more awesome because we found out that they're self-taught. Somebody tweeted this on the first day of the conference. They met Sandy Metz and found out that she was self-taught and they were so amazed that they said hero level up.  And this is cool. This is awesome. But because we value this self-starting so much, we are unfortunately increasing barriers to entry in formal CS programs. The rising popularity has made a lot of universities implement or re-implement depending on the school caps into classes and GPA requirements for the major. Which, you know, makes sense. You only want your best students graduating from this program because you want the best reputation. But that means the students who need the most help, the students who have the least technical background and can't get it because there are 500 people in CS 101, that means they're not going to get the knowledge they want, they're not going to get admitted into the major, we're increasing the barrier to entry for this field because you can't learn to code no matter what age if you don't have the right tools. I'm not talking about a computer and an internet connection. You can learn to code with a pen and paper. A girl, a woman that I know, is teaching underprivileged kids in Las Vegas how to code with a pen and paper. She's been doing that for a while. And now they have computers and they're writing code that's probably better than my code because they learned the logic before they learned the language. And that's huge. That's awesome. But having a computer doesn't automatically mean that you're going to be better than everybody else. It's about your educational foundations. And if you're one of those kids that graduated from a school where you can't read and write at your grade level, the likelihood of you being able to succeed in a CS program at a big university is probably not in your favor. And so to succeed as this industry, we have to stop expecting people to be able to teach themselves every step of the way. We still need to value this skill, but sometimes we need a little bit of help when we learn. And so if you really think about it from an individual person's point of view, can you say that anyone can learn to code to someone who has a developmental disability? My brother has Asperger's and there's no way that he could do this. But that doesn't mean that someone else who has Asperger's wouldn't be able to. Can you say that to somebody who doesn't have a home, doesn't have access to electricity, doesn't have access to pen and paper? Can you say this to someone who lives in America and does not speak English? Can you say this to a single parent who has no time for themselves? No, probably not. Unless they're Superman and then maybe. So what we really mean when we're saying that anyone can learn to code is that anyone with sufficient privilege, educational background and access to the right tools can learn to code, which is not as catchy. It's not great marketing. So we don't say that. We say anyone can learn to code and then we don't care about the people that the statement doesn't include. We don't care about the people we've forgotten. So it's not really for everyone. It's for the people who have the privilege of the access, the money. And that's where this all comes back together, the money. It's always the money because remember when everybody was talking about the American Dream? Everybody was talking about striking it rich or having a house. There's a lot of money in startups. October was the fifth largest investment month for the past two years. This last October, as in like two weeks ago, 1.2 billion with a B, billion dollars were invested in startups. That's so much money. And then when we talk about these boot camps, they all talk about how much money you're going to make when you graduate. You don't graduate, you don't get a certificate, but you're finished. I say junior developers make $73,000 after leaving Dev Boot Camp or $110,000 after leaving Hack Reactor or more than 70,000 after leaving Flatiron School in New York and good luck living in Manhattan on $70,000 a year. And code.org after Obama told us all to go there says that there are going to be $500 billion up for grabs and $1.4 million computing jobs. So clearly, the future is here, but we're not equipped to start it unless we go to Dev Boot Camp or we can change the world if we go to Starter School instead of selling sugar water or we can get the CS degree for the 21st century from the Harvard of Code Academies. We can get digital skills that are transferred to job skills. And if that doesn't translate into a job in three months of learning, they'll give us our money back. And this is a big reason why there's so much Boot Camp backlash. They're promising the American dream and they're not delivering. And of course not, it's a dream. You can't deliver the dream for everyone. It's just repackaging something that's been sold to us again and again and again. And it's there. It's waiting for us. The future is now. We just have to be equipped to get these skills. Give us your anywhere from $8,000 to $20,000 and we'll give you these skills. But you don't hear a lot about what happens if you're not successful after one of these programs. They don't talk about the students who are asked to leave because they're not good enough. That happens. They don't talk about the students who leave because the teachers aren't teaching them in a way that's productive for them. And so for these students, they don't have anything to say to them. And so they get this message that if they can't learn, it's their fault. And that this dream of a well-paying job and all this stuff is not broken. It's not unattainable. It's not privileged. You're the problem. You can't learn this thing. And that's a really bad way to teach people. And there's ways to fix this. There are a couple of things we can do. We can set realistic expectations for job seekers and skill seekers. If you're learning to code, make sure you know that you're not learning to program. You're learning to code. Improve our schools by education-focused initiatives and maybe volunteer to teach some kids how to read. I'm sure we've all got maybe, you know, five minutes for a week that we could help a kid learn how to read. Cut the hype and don't buy into the hype. The hype is part of the reason why these code schools are having such a hard time. And they're promising a dream and delivering a reality. And the American dream shouldn't be something bad. It shouldn't be something that, oh no, I didn't make it. Oh no, I didn't do it. Oh no, I failed out of this program. The American dream should be a positive thing. It should push us to do better. It should push us to try harder. It should push us to try new things. But when we promise the moon and never deliver it or only deliver it to one or two people and it's the same one or two people that you see over and over and over again on these websites, we're not using the American dream effectively. It should be a tool that pushes us to be better at what we do. So I hope that wasn't a super downer. But, I'm sorry if it was. So yeah, thanks.


Good morning traders, Patrick Munnoi here with another tip, little chart hits. I'm drawing your attention today to a developing opportunity in the Swiss Yen on the four hour time frame. You can see we're in an ascending trend channel and we broke out of that yesterday, got a bearish reversal pattern on the daily time frame. We're now just tested the monthly pivot and we're seeing some corrective action. So I'm looking for a move back up now into the 115.60 to 115.90 area. We're looking for bearish reversal patterns on the four hour time frame. Set short positions, targeting initially an equality objective down to 114.50. As always, traders, plan the trade, trade the plan and most importantly, manage your risk. Until next time, thanks very much.


Good. We will start the next set of talks. This is actually a set of lightning talks, and hopefully we will have some time for discussion as well. Very related. Lydia was just talking about Blazegraph. We are talking about using Blazegraph, among other things. The program today, I will give a short introduction to this kind of concept of WikiCite. I know many of you in this room are familiar of the people I know, but I will talk a little bit about that. And then we have four short talks, including a couple videos. We will play three of the lightning talk speakers are here. One is not able to be with us in person in Singapore. I hope we'll have time for discussion. There is a slide deck, which you can access. The link is here, and I'll show this link again. There are etherpads for this session. They're hard to find. The link is here. You can get it also out of the Wikimania Wiki. My name, by the way, is Phoebe Ayers. I am from Boston in the USA. I'm a librarian in my day job. And I have been involved in the WikiCite initiative for a few years now. So WikiCite refers to this community that's interested in citations. Bibliographic metadata is the term of art in libraries. And specifically citations and bibliographic metadata in the Wikimedia projects, in Wikidata, and in Wikipedia, and the other projects. The term WikiCite refers to a collection of projects, which we'll hear about a few of in this session, as well as the community. So this is an umbrella term for the umbrella community that is interested in citations. It's a loosely organized group of people, in fact, sort of self-organized at this point. Like, if you are interested in citations and bibliographic data, you are a part of WikiCite. Or you could be a part of WikiCite. WikiCite also referred to a set of conferences that were funded with specific grants that the Wikimedia Foundation got. Those are now finished, but ran from 2016 to 2020. There was a steering committee for those conferences and those grants, which I was a member of. And we had several good, like, strategy meetings and project meetings and hackathons in that time. The real meat of this is what are the current questions about WikiCite? So there's lots of questions. The big one Lydia already brought up, which is where should citation data be in the Wikimedia universe? Should it be in WikiData? Should it be someplace else? The question is relevant because, as we just heard, there's query problems, there's a lot of bibliographic data in the world. There's a lot of bibliographic data in WikiData. We might not have room for all of it. We might not have the ability to query all of it specifically. There's a lot that we don't have. So the question is how big do we go? And where do we put it? The very related question is how do we share this data across the project? So if you've got a Wikipedia and you want to use the citation that's in WikiData, how should that work for you? Or comments or dictionary. What tools can be built to understand citations better? The three, four talks will be about the tools that folks have built. And if we can understand citations, does that mean we can improve content quality also? Like can we understand misinformation better, disinformation better? Can we understand how to better trace the provenance of information on our projects? And how should we do that with data? I will say there's a vibrant community on the mailing list, on social media. There's a big telegram group that lots of people are in. James Hare, who many of you know from the Internet Archive and Wikimedia has also recently started monthly Zoom calls again. And you can join those. And there's a notice, there's a link in this meta page up here and they're also announced on the mailing list. I'll stop here and turn it over to my friend Diego. But before I do that, there's other talks on this program. Some of these have happened already.  And now I will turn it to Diego. Thanks. Oh, so we will save questions for the end. I think we'll have time to do four short talks and then we'll have a discussion, yeah. Okay, thank you Phoebe. Okay, so I will talk very briefly about a project, Wiki site related project that I worked in two years ago, it's called CITA. So in the scientific community, citations is of course a central part of it, just like it is in Wikimedia as well. We are always building on what previous scientists have built. So understanding citations is a very important part to understand how knowledge evolves in whatever field. And this data, like what papers site which papers, what works site which works, has become increasingly available in the open in the last years, thanks to the effort of, for example, the initiative for open citations, the open citations project, and much of this information is also available in Wikidata. However, when we started this project, there was to our knowledge, no popular reference management software, which is the software that scientists, journalists, et cetera, used to organize their bibliographic metadata. There was no such software that had support for citation graphs. So you would know like what is the title of a work, what are the authors of the work, but you wouldn't know on the reference management software what other works each work cited. So the idea of the project that was financed by a Wiki site grant, the year that the conference could not be held because of the pandemic, this was in 2020, is to bring support to Sotero for these citation graphs, either getting this information from Wikidata or adding that information manually. Also because visualizing, sorry, providing visualization support for this information so that it would be easy for users of the software to discover new relevant connected works and also provide a way for people that might be adding this information manually who are Sotero users, but who maybe do not much about Wikidata or are not active Wikidata editors to contribute this information they collected directly to Wikidata and make it available to other Wikidata or CTA users. Well, right now this software has been downloaded, I mean, we know it's being used by around 3,000 users. We are not sure about how this will continue being because of what Lydia and also Phoebe were saying about we're not sure what's gonna happen with the geographic data in Wikidata, but regarding those things, well, CTA, it was planned that it doesn't support only Wikidata but maybe also other bibliographic repositories and also the scope of CTA is beyond just journal articles. So yes, we do know that sometimes there is some replication between what is hold in Wikidata and what it's hold in like, for example, other repositories like OpenAlex, but there are other kind of work that are not available in other repositories like for example, very old books and maybe Wikidata can still be a repository for that, but that's of course a longer discussion related to what Lydia and Phoebe were talking about. And also we're not sure about long maintenance of the project, like every, or like many, sorry, free software projects maintained by volunteers. When the grant ended, maintenance rate decreased a little bit, then someone appeared that was a user of the project that actually took the maintenance of the project. I'm very thankful for him, Dominic Dalosto, but now also he's not a developer, I mean, full-time, he's a student, he's pursuing his PhD, so we're not sure whether he will be able to continue maintaining it in the future. So well, that's it about it, so thank you. And I think it's your turn, Jose Medina. Okay, thanks. Or introduce yourself and then we can play the video. Yeah, so hello everyone. So I'm Jose Medina in Turkey. I actually have another presentation within a few minutes about using LLMs actually to edit Wikipedia and Wikidata. Well, I will be talking a little bit about a thing that I have been working on for a year and a half, which is a collection for frontiers in Research Metric Analytics about how to use actually Wikidata to provide real-time research assessment of topics and scientists. So enjoy your time. My idea about integrating Skolia and OEKJ to enhance real-time research assessment of scientists, institutions, other citizens. So as you already know, Wikisite is an open database of bibliographic metadata hosted in Wikidata and it is mainly built based on crowdsourcing from open resources such as open citations and crossroads. And it includes bibliographic metadata such as citations, source titles, authors, publication years, external identifiers, et cetera. However, what Wikisite does not support is what is included inside the paper. This means the research metadata. For example, it does not involve the reused methods in research paper, the sample size, the main results, the target variables, et cetera. However, there is another resource called OEKJ, the Open Research Knowledge Graph that is an open database of LinkedIn Research Metadata is this structured in the RDF format just like Wikidata. Well, Skolia, as the tool, is a Sparkle-based dashboard for generated sentimental profiles, as you already know. And it is mainly driven by the data provided by Wikisite project. It provides statistical overview about publications, scientists, venues, prices, institutions at countries. But however, it does not involve statistics about the method used by scientists and institutions, the main research findings of scientists and institutions, et cetera. So what we can do here is that we can use OEKJ just to add new Sparkle queries to Skolia to actually cover these kinds of useful information. And as OEKJ is an RDF Knowledge Graph, it has its own Sparkle endpoint. And at this point, we can use the query federation with OEKJ just to generate federated queries and have integrated results that includes insights from Wikidata and OEKJ at once. So that's my main idea. I think that this idea is very important to bring Skolia and Wikisite to the next stage of the development of real-time research assessment. So if you have any question regarding this method, feel free to reach out to me. I will be happy to answer it, to answer all of your questions and mainly discuss more ideas related to such an integration of Wikidata with other resources to enhance real-time research assessment. Thank you. Super, thank you. And I think that... I think that we will have time for questions for our speakers who are here in person and then maybe we'll play the last video because Rod was not able to be online. So I wanna make sure we maximize our time with the folks who are here. So our last short talk is from SJ. Take it away. I love these.  And I don't know if people in the audience, how many of you have interacted with the Wikipedia 1.0 project? It's very federated, it's very decentralized. Wikiproject by Wikiproject, people go through and assess individual articles in the categories that their project cares about with regards to their importance and their quality. And every year, a couple of years, someone pings all the Wikiprojects and says, hey, can you guys go around and try updating and making sure that any new articles have a tag and then summarize that they run a handful of bots that summarize the current state for each project, products can compare, they can see how well they're doing. And it's just a very satisfying thing to have a nice report card. And the idea was for there to eventually be a Wikipedia 1.0, a nice snapshot of something that everyone felt good about. You could take the slice of articles that were above some threshold of quality and some threshold of importance and make a snapshot. In the end, I think we've ended up making a lot of offline snapshots that don't rely too heavily on that data, but some initiatives use this explicitly as how they decided what to distribute in different contexts. Nice things about this are it's very low key, it's very lightweight, most people don't notice it, and it doesn't really need any elaborate data structures, you're just adding categories to talk pages. So what do we do after Wikipedia 1.0? We're sort of there in a few dozen languages, we have very good coverage, citations. I think in the same sense, we should be assessing citation quality and coverage for all the articles. Most articles have a bunch of citations that have been assessed loosely based on the known reliability of the source domain, but not necessarily in terms of whether they include the claim that they're attached to. So just in the same way that we need to regularly go through other articles to figure out whether the quality has changed, you should regularly check whether the citation at the end of a paragraph still refers to what's mentioned in the paragraph. These are things that can be automated in the same way. We should, so I've been working in this arena trying to help build some of the tools that will be useful for getting this done, and I think if we did this, Wikipedia is already the best cited collection of knowledge out there on the web, which is a little odd because you would think that investigative journalists and other encyclopedias would really want inline citations, but for the most part, with a few exceptions, people are happy to just stick with some work cited at the end. So we should make more hay about that, and invite people to build tools like this. We already have citation bot which helps people clean up citations and make them beautiful. James Hare worked on a credibility bot that goes through all of the sources used as references in articles in a category, which would be very useful to Wiki projects.  How many articles were included? How many domains there were? How many of them are known to be reliable right now? That 3% is very low because a lot of these were archived, and so it's just the proximal link is to a DOI resolver or to some other redirects. So one of the things that I think caring more about citation reliability would get to is little things like fixing the citation template so that you're pointing to a non-redirect as an indicator of the source domain, or some otherwise describing the source domain. And we do have some mechanisms for flagging sources, and there are some more, like Aure's has probably not Aure's now. Some of the new automated tools that are feeding into enterprise flows are thinking about doing source evaluation, so it would be great for us to end up with a few more explicit flags. Right now a lot of the flags are embedded in user scripts and user widgets that people use for reading that highlight the sources on the page for how good they are. So I would love to talk to people who are doing work like this. I've been working on versioning tabular data with the underlay project for perennial sources tables to do things like map the different topic-specific perennial sources tables across Wiki projects into one another or into the global one. And WD Crap, which I invite everyone to contribute to, is the Wiki data product discussing source reliability, how we can improve it, what a shared schema might look like. So I hear that the Wiki data graph is gonna be split and that if it is split, it's probably gonna be bibliometrics that are split out that gives us an opportunity to figure out what a split could look like that includes other citation data. I would love to talk to people who are doing work on enterprise that are capturing some of these reliability signals and we could use some campaigns and some partnerships that help to gather information that's out there in the wild that's not yet freely available and say, please give us under the CC0 license all this data about anything cited. Don't want to repeat you. Thanks. So stay there, yeah. I think before I played this last video, which is from Rod Page, our friend and colleague Rod Page about Alec using Alec. Are there questions for Diego, if you say to me, or SJ about their projects? I wanna pause or me I suppose as your MC, but like getting references in, using references to understand articles, getting research information in. It's very relevant to my day job as a librarian. Any, yeah, I see one question here. Yeah, maybe two questions. One was about the reliable sources and getting more sophisticated about what is included in reliable sources than what is on the current list. So I'm interested in policy reports and papers and how they fit in. They're not generally very well cataloged, so it's difficult to find them. Often may or may not have URLs, et cetera, and quite difficult to assess. And I'm also wondering about shared citations how we get the citations that are actually on Wikipedia to be able to analyze them. Yeah, do you wanna stand up here? I don't know if we have anything. Starting with your last question first. When you say shared citations, do you mean the shared citations proposal from a couple of years back? Yeah, so my take is we should start by defining the Wiki base that will split out some of the existing bibliometric and source metadata that's in Wiki data, which is a different starting point from what was in the shared citations proposal. But there are already a bunch of properties about some of the things that you mentioned that you might care about. Domain reliability or non-domain if you don't really have a domain, some other measure, and there are some very underused properties that we could start using and then normalize a community around that. There's a discussion on the WED Crap Talk page which is like what are properties that you use for these sorts of things and can we make them better? What else do we need to add? Your first question was? So currently the reliable sources are considered to be books, journal articles, news articles. That's about it. Are you working on a policy paper project? I am. Okay, great. So I've seen that project, which is great. We should definitely do more things like that and there are many more countries. You were just working in Australia? Yes, but the project won't just be about Australian policy issues. So I'm Amanda Lawrence and I've just got a Wikimedia Foundation grant to look at policy topics. So rather than there's been studies on science and medical kinds of areas where you probably are more reliant on journal articles but the policy space is really a lot of organizations. Some of them are top of the line. Some of them are very dodgy but it's quite difficult. There's thousands of them, thousands and thousands of them. So the perennial sources is gonna be very difficult. Yeah, so I'll just say that the one parallel is preprint servers and they're increasingly fields on the cutting edge that publish almost everything through preprint servers. And if you look like archive.org in perennial sources is listed as do not trust, which really varies a huge amount with category and author. So let's talk about, I think we can do much better than domains in those cases. Yeah, either of you want to address that. I can notice they're talking about that. Reliable sources is a bit bigger than the data itself but is very related to how we get the data in. Like the kinds of data Diego's talking about like all the infrastructure has to support citing these things too, right? It's my take. Nicholas, and then I'm gonna play this video I think which is about five minutes. We'll go just like 30 seconds over. Yeah. Actually it's kind of a follow up question. Yeah. Wiki source. If when there will be a split in the graph of things like that, where will Wiki source and be ending? Because we need recycling. So many Wiki data and you want other type. So that's that. Big question. I think you should answer this question about Wiki source. I don't have an answer. But say again the question. I think that's again the same question I asked before to Lydia is how we prepare to that because there will be written on data somewhere. Obviously it's unavoidable. There will be point of pen where we have to choose. And my question is how do we prepare for that? Could Wiki site help also? Because Wiki source is not just me. Anyway. Wiki source. Just say one. Want to talk about Wiki source? This feels like a hallway discussion perhaps. I don't know. It is. Yeah, yeah, yeah. Let me, because I have a nice talk here from our colleague Red Page who could not be here in person. So I will play this about OpenElic. And then I do invite, there's a lot of people in the room here who have worked on citations. Andy Mabbitt has worked on citations that are relevant to your project. That's relevant for your question, Amanda. So there's a lot to say about Wiki data. I originally proposed this session for an hour and for discussion. And I wish we could have that, but we will have it tonight over karaoke instead. Citation karaoke. So thank you, everyone. Hi, I'm Rod Page. And today I'm going to talk to you about a little tool that I built called Alec. And this is a tool for visualizing information in Wiki data. So by way of background, I'm a biologist by training. I'm interested in biodiversity and taxonomy, study of classifying species. And I make a lot of use of the wikis. I use wikipedia, wiki species, and wiki data. And one of the things that sort of is challenging here is that there are lots of different wikis, lots of different kinds of information. And this information can be presented differently. So for example, if I'm interested in a monkey called the Bendered Suralol, which is found in Singapore and other parts of Southeast Asia, I can go to wikipedia or I can get lots of information that's aimed at somebody reading it. This is human readable information. I can go to wiki species, which is quite an interesting wiki. This is one of the few wikis that's focused on a particular topic, unlike most wikis, which are global in scope, but differ in language. And lastly, I can go to wiki data. Now, the interface of wiki data is very different. It's all about editing information. So it's great if you want to edit things but not so useful if you want to just find out what do we know. So what I decided to do is to make a little tool that can help me navigate information that's in wiki data. So this is a tool called Alec. Alec is an acronym. It's also my son's name. I still haven't figured out a good explanation with an acronym. What we're seeing here on the left is a screenshot from Alec that shows some information about the same monkey that we've been looking at. And this is all information which comes from wiki data. So information about links to other databases. If you want to learn more about that particular species, where it's found, other parts of its taxonomy, there are subspecies within the species. And most particularly, and the thing that I'm really interested in is links between this species and the literature. These are articles, scholarly articles, in wiki data that tell us something about this particular monkey. So this is a direct link to the evidence for that particular species. So under the hood, what's happening here? So Alec uses the wiki data query service, standard way of querying information in wiki data. When it gets the results back, it formats them in a particular way as a list, very much like something called an RSS feed. And then I take those lists and I format them in different ways depending on what I'm looking at. And as I said, my focus is on publications. I'm very interested in the wiki site project, which is trying to get the scholarly literature into wiki data. But I'm also interested in people and species and so on. So just some examples, this is how a journal looks. And you can see here, this is a journal and these are all articles. And when you see a thumbnail like that, that tells you that we have a free to read PDF for that article. So this is a way of showing you here are the articles for this journal. And by the way, these are the ones you can have a look at and read. Academic journals often have a very complicated history and wiki data is great for this. This is the sort of family history of one particular journal. So the French journal called Adamsonia and it's been renamed and there's all sorts of very complicated things going on here. This is the way to summarize that history which is all documented in wiki data. And also, taxonomy doesn't happen without people, without taxonomists. So I'm very keen in helping document those. This is one taxonomist, this is the late Vicky Funk and you can see there's a page here. It has information on her publications but also information on publications about her. So these are obituaries. For example, published shortly after her death. So if you're interested in understanding the impact of this particular scientist, wiki data would be a great place to start. So just to summarize. So the underlying goal of this work is to document biodiversity in particular by uploading publications about species and linking those to the species but also to the people who publish that information to the specimens that taxonomy is based on, to museums, draw some wiki data and so on. I need a tool to help display the results but also to find gaps. And that's what I like to us for me. If I go through a journal page in Alec and I don't see any articles, I know that's a journal that needs work. And if you find any of this work of interest, then I invite you to go and try Alec at this website here. Thank you very much for your time. Thank you all for joining us. I'm sorry to eat into your break. I would encourage everyone to, I'm going back to the beginning, to join us in the wiki site conversations that happen online if you're not already in these places and you're interested in these topics because as you can tell, there are many, many, many very cool projects that are happening. So it's break now, right? Until 3.30? True? Okay. So we have a like 10 minute break but if there are questions, again, for any of our speakers, Sam, who's in mean Diego, feel free, we can merge this into the hallway. Anything pressing? All right, join me in thanking our speakers. Thank you.