 It's always hard to open the second day of a conference because there's all the drinking the night before, and I get you might be a little fragile, but I'm going to ask you to do one thing for me, which is toward the end of this session, I'm going to ask you to sing. So hold on to that. If anyone who doesn't want to sing, it's time to leave now. Can data do more than describe? Might it also diagnose and propose ways to intervene? What happens if we move from making our data more open to making data that produces openness? How do we use data to open ourselves, for example? What you can see behind you is an artwork by Anish Kapoor from the High Art Museum in Atlanta. And I'm going to speak a little bit about artworks to kind of start to begin to convey ideas inspired yesterday by Chris and the Vermeer painting. So I'm hoping you can see some continuity between yesterday's talks and today's. I like this work a lot, not the list of which because it's an ocean of images of me. And anyone who follows me into it will know that I'm fond of a selfie. But I also like it because it's kind of inspiring of a problem that has occurred in my home discipline of cinema studies, which is the persistence of the image of a woman in a mirror. And now that I've said that whenever you see a film now, you will see an image of a woman looking at herself in a mirror. And there's many, many films that do this and you can probably think of some off the top of your head. Any takers? There's many famous fairytale ones. Yes. There's another really, really important one which is The Lady from Shanghai, which is a fabulous film which features a hall of mirrors and a woman kind of being completely lost in the hall of mirrors. But the film that this image behind me really reminds me of is the film Being John Malkovich. Do you remember that scene in Being John Malkovich where he's sitting in the restaurant and he looks through his own eyes out into the restaurant and all he sees is images of himself? It's a really kind of devastating scene in a way and it encapsulates for me a really interesting problem that inflects a lot of academic work and I think a lot of work in the glam sector which is the problem of how we get outside our own heads. How do we do that? How do we get outside our own heads? How do we open ourselves to what we don't already know? To uncertainty or to alternative worlds. How do we engage with and alongside people who aren't the mirror image of ourselves? How do we make connections? And I mean that in two senses, in the sense of making connections at the level of ideas, ideationally, and also relationally, how do we make connections based on who or what we don't already know? And this is a really challenging question for people who build digital infrastructure, particularly in this sector. How do we take seriously the lives of others? Not at the level of beliefs, but at the level of meaning. Not by explicating their worlds on their behalf, but rather by multiplying worlds. By enabling a world in which we can all live our lives as variously as possible. How do we do that? How do we do that in ways that acknowledge that we are actually changed by that process? How do we open ourselves to that change? So how do we work in our sectors in a way that responds to those questions? That's what gets me up in the morning and generally prevents me from falling asleep at night. Because we live in an era of devastating and widening division. And I think in this era these questions become even more important. We live in a closing down world. This is a world where solutions for really critical social problems come up like build a wall, incarcerate people on the islands, including children. This is not the kind of solution I want to see for the really significant, devastating problems that we face at the moment. We have to, in everything we do, redress the divisions that mark this world's version of humanity. And we can do that by focusing on our capacity for openness and connection. At the political level, at the institutional level, at the technical level, the social level, organisational, personal, environmental, you name it. We all have these opportunities to think through these problems in relation to ourselves and where we fit into the world around us. So to me these are also questions about how we handle data and the infrastructure that we use to fathom the data we work with. They're questions that are fundamental to infrastructure because what is infrastructure except the enabling of the condition for the possibility of connection. All infrastructure enables the possibility of connection. That's what it does. And at the moment we have a very limited idea about what is possible in terms of those enabling conditions because it's largely built for us by engineers. And we're missing whole other ways of approaching the problem of connection. I'm really interested in this because in terms of the history of the building of infrastructure, we have many precedents. If you think about, for example, the great era of maritime infrastructure that the Dutch built in the 17th century, they were also building the conditions for the possibility of connection. And they did it in a way where they felt that they needed to answer four key questions. And to some extent those questions work quite neatly across to the way we've approached digital infrastructure as well. So when you think about that, that's a bit disturbing because that era is the era of grand imperialism. So we've taken a lot of our metaphors from a very problematic period where we use words like discovery and navigation and we apply them in our current context. What were those four key questions? They're very interesting. The first one was where am I now? So the Dutch needed to build technologies that could solve the problem of how to identify where you were at any given point in the world. We kind of do that quite well in digital infrastructure. You can work out where you are. The second one was where am I going? The internet kind of does that okay. I sort of think that Tim Berners-Lee gave just a very dumbed-down version of where are we going? You don't really, when you click on a link, you don't really know where you're going. Anyway, that's another kind of detailed debate. Third question that the Dutch needed to answer, what is my speed? We do that pretty successfully. The fourth one is the key one. The fourth one is, what is my depth? What lies beneath my feet? What came before me? What provides the conditions for meaning and understanding? I don't think we do that very well in the current way we build digital infrastructure. I think we can do a lot better on what is my depth. And I think it's a very evocative question at lots of levels. Fathom, which is the way the Dutch would have measured depth, fathoms, actually comes from an old English word that means arms outstretched. So when we talk about how we can fathom something, a concept or an idea, we're talking about what lies in our grasp, what's within our reach. And I guess, for me, the critical question is how we actually think about what's out of reach, not just what's within reach. I want to expand that notion of fathoming in a way. I'm very interested in this idea of what's beneath our feet. Anyone again who follows my social media will know I'm very fond of a shoofy as well as a selfie. This is my shoofy from arriving here the other day. This is Wellington Airport, which has the best airport carpet in the world. And I love this image of Wellington Airport because it's kind of an extension of what we've just been talking about. It's this idea that you fly in, you're completely discombobulated by the experience of flying. You're out of your jet lag, your bodily relationship to space is also completely disrupted, and you land and you land on solid rocks, rocks underneath your feet. And I love it because it's a kind of a footnote to this lecture as well. It took me a long time to find my feet as an academic and as a speaker. And I'm really interested in this idea of the modern equivalent of finding your sea legs or finding your land legs or finding your feet. And I'm particularly interested in this idea in relation to Wellington Airport because I spent a lot of time at a previous visit to Wellington staring at this carpet because of a particular incident that occurred at the airport. So I was waiting to board a flight, I'd been to a conference in Dunedin, standing there, suddenly heard my name over the PA system. Would Dip for Hoven, please come to the chicken disk? I had no idea. Seriously, I had no idea. I thought they were gonna feed me chicken. I was a little bit hungry. I finally worked out, they met the check-in desk, and I went to the check-in desk and they cornered me and two staff came over and said, are you Dip for Hoven? And I went, yes, that's my name. They said, show us your passport. So I did, and they go, right. Have you at any time ever been known as or are currently known as David? And I thought about it. And I said, yeah, I've been called many things in my life, but I'm pretty sure that's not one of them. And they went, really? Are you sure? And you know, I'm pretty sure. Half an hour later, two policemen in an interrogation room, and I finally got out onto the flight, at which point, of course, the plane was very late and people applauded, and I was hideously embarrassed. And I will tell you over lunch how I got out of that predicament. That's not the point of today's lecture. But I'm very interested in this idea. It involves Christ's church. Of how the realization occurred to me that I had spent the previous two weeks in New Zealand walking in someone else's shoes, a man named David. And I had done that inadvertently. And I suddenly became aware of the precarity of my standpoint. Was I Deb? Was I David? I don't know. Depends, who was asking, and at what point in my trajectory of my visit to New Zealand I was standing. And I want to kind of play with that a little bit later in the lecture. So hold on to the idea of the Davids, because they're going to come up. And this idea of the extent to which we are an amalgam of not just what we think we do, but what is done to us. So that weird embodied entanglement of both what we do and what is done to us. I want to talk now a little bit about the digital humanities, which is the discipline that I find myself working in mostly at the moment. A lot of people are very confused about what the digital humanities is. There's many books written about this, many articles. It's a vast topic of debate and conversation within itself. I always like to think of this in terms of a couple of jokes. So I'm going to ask them these jokes because I'm a humanities scholar, they're questions. How many digital humanities researchers does it take to change a light bulb? Any takers? Two, one to change a light bulb and one to tell you that you aren't a real digital humanities scholar unless you made the light bulb yourself. So what this tells you a little bit about the digital humanities is the idea that it's the digital humanities is different than the general or typical humanities in that it's much more about collaboration and much more about making and doing, that it has an intense interest in the methods that we work with. Because I'm a humanities scholar, I'm going to ask the question again. How many digital humanities scholars does it take to change a light bulb? And if you're thinking the answer is two, you're probably a scientist and you're looking for reproducibility. And I can tell you now that in the humanities we have a word for that, it's called plagiarism. The answer is one. But no humanities scholar is ever going to trust a single light source. And what that tells us about the digital humanities is that we are adding to the underlying values of the humanities, non-trivial computational studies. So we want to see those traditional values of the humanities flow through into the digital humanities. And what are they? And they are things like collaboration, contestation, complexity, connectedness, coexistence, non-logical connections like serendipity and so on. These are the things that we value in the humanities in terms of how we work. It's not about one size fits all, it's not about the only measure of value being efficiency. And yet that's the kind of infrastructure that is built for us constantly and that we find ourselves breaking or attempting to break. And what kind of... Oh, here's a nice slide, this one's about complexity. And it's like, it's another artwork. I've tried to summarize some of this in a visualization because it's often very hard to talk about this stuff. So you might, these two panels in the middle are the famous gaping void panels about knowledge and information, to which I've added this one here and this one here. So humanities knowledge, in a sense, is a non-hierarchical set of connections that are of varying degrees of viscosity and thickness and richness and color and so on. So I think often this works better for people than an elaborate explanation of how the humanities does things. So what I want to do is talk a little bit about those values in relation to one piece of digital infrastructure that I've built, which is the humanities networked infrastructure, or honey. I'm going to skip through this fairly quickly because I want to get to the daves because they're much more fun. So here's typically how we engage with search infrastructure using Google and of course we all know the limits of the single search box. This is probably the room where I'm preaching to the choir. We know that, for example, Google can't show us the relationships between records in a list that it provides. It can't distinguish entities semantically. It systemically elides the distinction between language or the linguistic and the semantic. For example, words become analogs for concepts. These are ways of limiting the kinds of things we think we can search for. And they're very frustrating to those of us that see those distinctions. Humanities scholars in particular. Because of this, a group of researchers, people who were working already in digital infrastructure in the humanities in Australia, collaborated to build this, the humanities virtual laboratory, the national humanities virtual laboratory. And we did it in a way that attempted to encapsulate our preference for working with complexity, with contestation, with the value or sensing the value of coexistence in data records or collections as data. So Honey was specifically designed to support non-linear research methods to enable heterogeneous metadata to speak to each other without losing the unique nature of each discipline resource. So valuing provenance. Enabling researchers to create their own ontologies, to create links between records or entities using their own words. So what that enables is multiple views of the world to be encapsulated in one data set driven from the bottom up. So we call this vernacular ontologies. So this is a way of enabling communities to reposition not the description of the data or the metadata record, but the actual connections built between them. A fairly radical concept and kind of very hard to get your head around. So it's an abandonment of structured data or structured vocabularies. And it replaces those with opportunities for researchers to be expressive, to capture the ephemeral, and to contest, to say not. This record is not related to this record. Saying not is so important. You have no idea how important it is to researchers to be able to do that. And yet we can't, in the vast majority of digital infrastructure systems that we are delivered, we can't do that. You can't resist, you can't contest. There's a quick summary of honey as it stands at the moment. And one of the interesting things about honey is that it's built on a graph database, so it enables researchers to follow trails of relationships. When we first built it, we tested it on a scientist, and that's what they did with the graph. I love, this is Dr. Spacejunk. I always liked a good credit for this, because someone inevitably tweets her, saying that lecture again. That actually broke honey, because we didn't actually think anyone would do that, because typically what a humanities researcher will do is this. And they will enable you to follow trails of relationships which are built by researchers. So every connection in here has been written by a researcher in their own words. That's my collection, a collection that I built around Ken Hall, a famous, well not very famous actually, Australian filmmaker, working in the 1930s, the most profitable film director in the history of Australia. And I made a collection, I added some records, I described the links between the records, and as I was doing this one day, I realised that I was suddenly connected to someone else's collection that I hadn't built. Someone else built that. Serendipitously, we discovered that honey enabled serendipitous discovery. Which is the only way you should discover serendipity. Serendipitously. So this in a sense emulates that problem that we've had in trying to enable people to find relevant and significant knowledge that wasn't what they already knew. So serendipity is not about accidents. People think it's about accidents. It's about accidents and sagacity in the original formulation of the words. So you have to know that what you've discovered in order for it to be meaningful. It's not just about tripping over something and going, I tripped over something. You have to know that what you tripped over is important to what you're trying to think through. So for example, when penicillin was discovered because someone left a moldy dish out, my dishes go moldy all the time. I have not yet discovered penicillin. Enough of honey. Let's move on to the second of the two mini keynotes that you're getting all in one, which is a discussion of some of the work I've been doing with my research team, which is called the Kinematics Group. Kinematics is a group of researchers working principally in relation to one data set. But we do a number of different projects with that data. Some are our music industry, so it's a little bit different. There are other things to do with cinema showtimes or the places in which films are screened and when, where and when films are screened. So this is a visualization of our data set. It has about 350 million showtimes in it. So it's every film that's screened in every country in the world for two and a half years. And when I say every country in the world, clearly it's not every country in the world. There's big gaps. But we do cover about 95% of the openings in that period. And it coincides with the release of the Hobbit. We started collecting data with the release of the first Hobbit and we finished at the end of the release of the third Hobbit. So it has a kind of connection to New Zealand in a way. So is 350 million records big? Is this big data? Well, not if you're an astronomer. Probably not next week. People are very obsessed with this idea of big data. I find this very funny, interesting, well not very interesting conversation actually, because it's not how big your data is really, is it? It's what you do with it that counts. Thank you. But if that's the case, then what is big data? So big data really needs to be understood as a collection of data that in any given context is so large, it's ungraspable to get back to that metaphor of the fathom. And it's incomputable using conventional techniques. You have to work outside your already existing frames of reference and methods of working to work with it. So in a sense, if your data hasn't caused you to reconsider your place in the world, it probably isn't big data. Okay? If your data hasn't caused you to have an existential crisis, it probably isn't big data. And I'm going to give you an example of that distinction. In about two years ago, the UN released its data on forcible in the world. 65.3 million people were displaced. That's an extraordinary number. That's so big that the UN tried to make it sensible to people. So they said that's actually 24 people forcibly displaced every minute. And that is 12 people every time you breathe. Sorry, two people every time you breathe. When you hear that, if you catch your breath, then you're thinking of it as big data. If you hear that and you try and calculate how many times you breathe a minute, then you're actually thinking of it as a factoid. That's a small data approach to the problem. Okay? And that's the distinction I want to make. Big data is monumentally detailed and infinitely interconnected. It's ramifying and it has ramifications because it implicates us in ways that we don't always necessarily understand. It has epistemic implications. It pushes at the edges of what can be known and how we know it. As well as ontological ones because it requires us to actually rely on machines and people outside our own frames of reference to work on it. You can't work with big data without yourself leaning into the interconnected world and without recognizing that your disciplinary outlook is always and in every aspect touching some other. Big data, for me, has been a revelation. Working with big data has been an extraordinary experience. One of the projects we worked on I said earlier was the Hobbit project and what we're trying to do is understand how the Hobbit moved around the world. What we discovered after an enormous amount of work and effort was what we already knew. The Hobbit went everywhere really quickly. I could have told you that without working with a huge data set and 12 other people. This is my existential crisis in front of the data. I said they're going what am I doing? Why am I wasting my time working on all this data that is only going to tell me what I already know. What would happen if instead of asking a small data question of a big data set, I changed the question. What if I tried to find in the data not the world that I was describing as I already knew it but the world I wanted to see? What if I asked that of the data? So what world did I want to see? What I wanted to see is a world that wasn't defined by practices of domination. Okay. The data that I was looking at was all about Hollywood and how Hollywood went around the world. How would I find something else in the data? So what we decided to do was look for reciprocity in the data. Not domination. And so this is what we found. These are, this is a network visualization of countries on the basis of their capability for being reciprocal. For trading evenly. And here are some of the kind of key dyads. New Zealand comes up quite a lot in New Zealand. It's a very good player when it comes to reciprocity. So Greece and Venezuela trade films at about the same rate. Spain and Greece, Australia and Germany. New Zealand and the UK, surprisingly. Are fairly reciprocal in their relationships. So this is just one way of rethinking how you might question the data from the perspective of your values not from the perspective of what you already know. These are the people I work with. I just want to give them a shout out because when I talk about this I really am talking about a team effort. It's not about me. And we work across a huge range of disciplines. So Colin, for example, is a geospatial scientist. Stuart is a network engineer. Bronwyn is a cultural economist. Ben does cultural policy analysis and so on. You see we have a rich range of disciplines. Michelle is a visual artist and Lachlan is just a guy who is in the audience for one of my lectures and was so outraged he went and did data analysis for me. And I always just like to give him a plug. Okay, so now we're going to get on to the Dave's. This is the project we've been working on most recently and the one that has created the interesting kind of global kerfuffle. And it starts here for me. Well, it sort of starts here. This is me sitting in front of probably the most devastating painting I've ever had the opportunity to look at. The painting is by Turner. You probably guessed that already. And it's a painting of a shipwreck painted very late in his life. It's unfinished. He couldn't finish it. And it depicts not just an abstract shipwreck. It depicts a very specific shipwreck. This is the wreck of a convict ship heading to Australia in 1833. And the convict ship floundered just off the coast of France and the French could see the ship sinking into the captain and said, we'll help, rescue the drowning passengers. And the captain said, no, my duty was to deliver these passengers to New South Wales. And since I'm no longer going to be able to do that, I have no more responsibility for them. And he let them drown. So 108 women and 12 children drowned on the ship. There's so much about this painting that for me resonates with a contemporary global callousness in the face of desperation at sea. It's very hard to look at this painting and not feel the contemporary resonances as you look at it. And there's also in it Turner's own dwelling on the sub in the sublime and how he has created a world without a fixed horizon. It's a world without a moral anchor. It's a world that we're trying because it's asking us to reflect on our own shifting proximity to depth and oblivion. What's beneath the feet. And it's no surprise that Turner couldn't finish it. It's sort of a painting about representation itself collapsing. There's no perspectival or moral anchor in a painting by a man who was for 30 years a professor of perspective. He's challenging us with a perception somewhere in the incoherence of what he's depicting. And he's drawing you in from that very specific shipwreck to a broader notion of an existential crisis which is in a sense what he's facing as well. How do we address our own disorientations and our own entanglements as that relationship between what we do and what is done to us and how we are rendered visible and invisible in a sense. And he's asking us to consider this in relation to our own spectatorship because where the captain looked away the painter looks but what do we do? Do we walk away? People walk past suffering all the time. Suffering has its own kind of sense of distraught loneliness, I think. And this painting tries to capture some of that. There's a theorist called Hans Blumenberg who wrote a book about shipwrecks and spectatorship and he says that what's interesting to him is that the spectator who witnesses distress at sea is the embodiment of a definition of theory. And I think Turner is in a sense asking us to reflect on that position. To what extent are we just the theorists, the spectators, the standards by? Do we just walk away and console ourselves with a little bit of theory? Turner himself of course doesn't turn away and the image includes this visual turning which is also kind of telling. There is something he doesn't see I think in the painting but which is really interesting for those of us on this side of the world when we look at it which is that of course that shipwreck is also part of a much larger problem which is the problem of colonialism. So after I looked at that painting I turned the corner and I saw another Turner because one good Turner deserves another. Come on. Alright. Shipping infrastructure in the 19th century in England was a subject of enormous concern because there was a lot of loss of life. The Turner painting is one amongst many images of shipwrecks that he himself made but they don't even reflect to any extent the difficulties experienced in shipping humans from one place to another in this period. There were two really interesting open data initiatives that helped solve this problem or to make shipping infrastructure more safe. The first was initiated by Robert Fitzroy the guy behind me who was appointed as the director of the meteorological office in the 1850s and he gives us the word forecast. He's the guy who invented forecasting somewhat humorously at the time because of course it went really wrong but eventually they sort of started to get it a little bit more accurate. The second guy is this guy Samuel Plimsall Samuel Plimsall was a very interesting guy. He's a gift of feminists all over the world because he wrote a book called Our Seaman and I am going to talk a lot about him in relation to feminism. He was very exercised by a particular problem in shipping infrastructure which was the deliberate sinking of ships for insurance purposes often with great loss of life. The coffin ships that would sail down the Thames and would just sink disastrously because they were grievously overloaded. He invented what we now know as the Plimsall line. Has anyone got converse shoes on today? Come on someone has to, yes thank you if you look at your feet see a line on your shoes which is why they're sometimes called plimsalls so if you were to put your foot in a puddle and the water went over that line your feet would get wet. The Plimsall line has a contemporary fashion statement. What can I say? I'm interested in these two guys because they've informed my work at two levels. I want to ask can we preempt imminent disasters using data? Does patriarchy have a Plimsall line? And if it does or if it doesn't how do we sink the ship SS patriarchy? The reason I'm interested in this or there is the Plimsall line. That's what it looks like. This is a picture I told you like selfies that's me in 1988 protesting the closure of the women's film fund in Australia because apparently we'd achieved equity in 1988 and as you can imagine I was a bit annoyed about this. Perception and we did manage to keep it running for a little bit longer. This is a film festival that I organised around the same time. I put that up there just to show you that some feminists could design and some couldn't. I'm sitting at my desk around the time of first encountering the Turner painting and incomes the data from Screen Australia about women's participation in the film industry and guess what? It was pretty bad. Guess what? It wasn't just pretty bad. It was worse than when I was standing there in 1988 protesting. Okay? In 20 years 30 years. In 30 years the data had not just not improved it had gotten worse. And I had a complete and absolute incandescent meltdown at my computer. And a whole bunch of things occurred to me at once. Firstly, I failed. I failed as an activist not a single thing. Not a single thing. Now you have to understand I was probably also having a midlife crisis. Could have been a menopausal I don't know, it was a bit hot. And what was clearly going on for me was that weird combination of the midlife crisis of hubris and despair all together. It really wasn't just about me, it felt like it. It really felt like I had failed and it occurred to me suddenly that there were two things I wanted to do and I did them both. The first is I vowed never to release the data again. Because every time we release this kind of data it affects women's belief in their ability to be treated fairly. So what that tells you is that data has a sort of power so let's just put that up here for a minute and the second thing that happened is I went we are looking at the wrong problem here because what we keep doing when we see this data is we keep saying women have to change. We need to give them confidence training and some development funding and we need to I don't know, make them work with each other. I think that was one of the strategies that Screen Australia came up with. Guess what? Women are not in a position to change the industry because they're not actually participating in it. Who is in a position to change the industry because they are the beneficiaries of the industry's lagesse. Who is that? Men. We never ask men to change their behaviors. We don't study them. We don't look at their behaviors. We don't know how they behave. So I went I'm going to do that. Here you go. This is a network visualization of the Australian film industry divided by gender. Red is men, blue is women. It's a bit hard to tell on this screen because the red and blue is not very clear. Each source node, so network visualizations are made up of what are called nodes and edges, dots and lines. Each node which has a clockwise line coming out of it is a film producer who is responsible for recruiting the creative team. So the producer recruits the writer and the director and the other producers. And what we'll see here if you look closely is a lot of all red groups. They are men who only work with men. Guess what? 42% of all male producers in the Australian film industry over 10 years only worked with men. 75% worked with 0 to 1 woman. Who are those men? Here they are. I call these men the gender offenders. Because what I did with these men is something very specific. I sat down and I went who looks at data to produce change in the world? And there is an answer to this. Who looks at this kind of data? The police encounter terrorism agencies. And what they do is they identify the key players and they take them out. And it occurred to me that we could do the same thing. I know that man's name. I know his name. I know their names. Here is a policy solution. What would happen if we just stopped funding men who don't work with people who don't look like them? It wouldn't cost anything and you could start doing it tomorrow. But we don't do that. Why? Because we're more invested in lip service than we are in actual change. All of us. That's the German film industry. Red is men. These are the gender offenders in the German film industry. That's the Swedish film industry. These are the gender offenders in the Swedish film industry. That is my world. That is Australian research funding. Red is men. I saw this visualization and cried. And then I left the country. That's me. That's my data selfie. Because this is really about selfies. This is the whole lecture. These are the gender offenders in the Australian research funding. Average team size is 12. These are men who don't work with women in an average team size of 12. How much willpower does it take to set up a team without a woman in it when you've got 12 opportunities to fill that place? You might think patriarchy looks like that. You might think it looks like that. You might think it looks like that if you're in Australia. Actually, patriarchy looks like this. That is patriarchy. What does it mean to give weight and shape to relationships? Can we recognize the quality of relationships by their shape? And if the answer is yes or even maybe, what are the implications of that? For how we understand ourselves and for how we address uneven patterns of interaction and coexistence in our day-to-day lives. What if we could see the contours of injustice? What if we could bring into sight structures of domination between people within organisations, for example? So that there would no longer need to be the vestiges of trauma to produce the evidence of its existence. So that women and other minorities wouldn't have to provide evidence of discriminations perpetrated against them. So we don't have to wait for them to leave to be ill or worse. What if that was the case? How could that lead us to understand the intractable, the seemingly intractable inequities as systemic and individual, rather than natural? If we look really quickly at the question, Plymsa line? There you go. That looks like there's a Plymsa line, doesn't it? A very similar set of statistics at the aggregate level. Same. No, not true. And there's no Plymsa line and I'm going to show you quickly and then I'm going to wrap up why that's the case and hopefully we'll still get time to have a little scene. Look at this data. It just looks so consonant. This is in the research field. These are the most commonly funded names in Australian research. What is the most lucratively funded name in Australian research? It's a pun. It's not David. It's Richard. David Richardson. Do we have a diversity problem? No. We don't have a diversity problem. Diversity is never the problem. Women are not the problem. Minorities are not the problem. We don't have a diversity problem. We have a diversity problem. This is why it's not actually all about the aggregate. Underneath the hood, these networks display very similar properties. There is no one size fits all solution even though it looks like there might be at the aggregate level. I'm just going to skip. Oh, and this is what we then did. We modelled what would happen if you took all the gender offenders out. How many men do you have to take out of a network before it changes? All of them. Probably not a practical solution, as desirable as it might sound. And we'll get to the solutions another time. We did then build a tool to do that modelling so we can actually test people's policies, gender equity policies, for effectiveness. And what you will find is that this idea of just chuck women into the network and stir, surprisingly doesn't work. Taking the David's out doesn't work either. The only thing that will work to produce change in our lifetime is for people in positions of power to collaborate with people who do not look and sound like them. That is the answer. We have modelled it. We have tested it on thousands of different industries. It is the only solution. Okay. We're going to skip Screen Australia and their naughtiness. These are some of the responses that I've had other than Screen Australia's fairly negative ones which I've just skipped through because... This is an 11-year-old who sat in the audience and actually drew an alternative visualisation of the network which I really thought was sweet. And this is our sing-along. Alright. Now, the thing is you'll probably forget most of what I've said because that's what happens with lectures unless I plant an earworm. And that's what I'm about to do. So I want to hear strong singing. This is going to lodge. You ready? Sound, please. I'd like to tell you about the Dave's I Know. It's pretty easy to follow. David Hoffner. He works in my debt store. He's worked for 12 years. He'll probably work here for more. These are the Dave's I Know. These are the Dave's I Know. These are the Dave's I Know. I've known since I was six in grade and he broke his legs so we got drunk and sick. These are the Dave's I Know. These are the Dave's I Know. These are the Dave's I Know. Dave Jadiski. Man, this cat can swing. He weighs almost 50 pounds and he delivers my paper on time. These are the Dave's I Know. Capesano. I hardly know him. All right, just to finish up. Are all Dave's the same? Are they gradations of Dave-ness? How are Dave's linked? How do different Dave's interact with each other? How do we recognize the Dave that lurks inside us all? Our own Dave-ishness. These are questions to ponder as you kind of take this away. This is going to be my last lecture for quite a while. I'm taking a break from giving public presentations and I started today with an image of my feet on the Wellington carpet and I kind of did that deliberately for a number of reasons and you would have followed the trajectory of the standpoint foot metaphors all through the lecture. I want to adopt a new way of thinking about how I speak and I'm not sure this is the one for me. I want to think about what it means to speak nearby rather than with or at people. Speaking nearby is something that I think is an unfolding kind of experiment for me and I wonder if it's something that we might all contemplate. It means not pointing to an object as if it's distant from the speaker or absent from the place where speaking occurs. It means speaking in a way that's not entirely outside one's self. It means a speaking that reflects on itself and can come close to a subject without seizing or claiming it. It refers to the life around the speaking while at the same time having its own life. So it's a sort of speaking relationally. And it asks us to invite accidents or incidents or things we haven't thought of before into conversations. And I think that's where I need to be. So I'd like to thank you for being my last lecture for quite a while. Thank you.