At this panel discussion Trevor Chow-Fraser of the Office of Sustainability announced the release of Moving Ideas Without Moving People a toolkit on running e-conferences at the University of Alberta. This toolkit was co-authored by Trevor Chow-Fraser, Chelsea Miya and Oliver Rossier and was based on the KIAS experience organizing our Around the World e-conferences.
What is at stake is the greening of research. We need to try and adapt different forms of video conferencing and live streaming to our conference/workshop needs in research. We need to depend less on F2F (face-to-face) conferences where everyone flies in. We need to confront the carbon costs of flights and how habituated we are to flying for research.
The Guardian has been reporting on Cambridge Analytica for some time – see their Cambridge Analytica Files. The service they are supposed to have provided with this massive dataset was to model types of people and their needs/desires/politics and then help political campaigns, like Trump’s, through microtargeting to influence voters. Using the models a campaign can create content tailored to these psychometrically modelled micro-groups to shift their opinions. (See articles by Paul-Olivier Dehaye about what Cambridge Analytica does and has.)
What is new is that there is a (Canadian) whistleblower from Cambridge Analytica, Christopher Wylie who was willing to talk to the Guardian and others. He is “the data nerd who came in from the cold” and he has a trove of documents that contradict what other said.
It is difficult to tell how effective the psychometric profiling with data is and if can really be used to sway voters. What is clear, however, is that Facebook is not really protecting their users’ data. To some extent their set up to monetize such psychometric data by convincing those who buy access to the data that you can use it to sway people. The problem is not that it can be done, but that Facebook didn’t get paid for this and are now getting bad press.
The question I want to explore today is this: what do we do about distant reading, now that we know that Franco Moretti, the man who coined the phrase “distant reading,” and who remains its most famous exemplar, is among the men named as a result of the #MeToo movement.
Lauren Klein has posted an important blog entry on Distant Reading after Moretti. This essay is based on a talk delivered at the 2018 MLA convention for a panel on Varieties of Digital Humanities. Klein asks about distant reading and whether it shelters sexual harassment in some way. She asks us to put not just the persons, but the structures of distant reading and the digital humanities under investigation. She suggests that it is “not a coincidence that distant reading does not deal well with gender, or with sexuality, or with race.” One might go further and ask if the same isn’t true of the digital humanities in general or the humanities, for that matter. Klein then suggests some thing we can do about it:
We need more accessible corpora that better represent the varieties of human experience.
We need to question our models and ask about what is assumed or hidden.
Last week I presented a paper based on work that Stéfan Sinclair and I are doing at the University of South Florida. The talk, titled, “Cooking Up Literature: Theorizing Statistical Approaches to Texts” looked at a neglected period of French innovation in the 1970s and 1980s. During this period the French were developing a national corpus, FRANTEXT, while there was also a developing school of exploratory statistics around Jean-Paul Benzécri. While Anglophone humanities computing was concerned with hypertext, the French were looking at using statistical methods like correspondence analysis to explore large corpora. This is long before Moretti and “distant reading.”
The talk was organized by Steven Jones who holds the DeBartolo Chair in Liberal Arts and is a Professor of Digital Humanities. Steven Jones leads a NEH funded project called RECALL that Stéfan and I are consulting on. Jones and colleagues at USF are creating a 3D model of Father Busa’s original factory/laboratory.
David Sepkoski has published a nice essay in Aeon about What a fossil revolution reveals about the history of ‘big data’. Sepkoski talks about his father (Jack Sepkoski), a paleontologist, who developed the first database to provide a comprehensive record of fossils. This data was used to interpret the fossil record differently. The essay argues that it changed how we “see” data and showed that there had been mass extinctions before (and that we might be in one now).
The analysis that he and his colleagues performed revealed new understandings of phenomena such as diversification and extinction, and changed the way that palaeontologists work.
Sepkoski (father) and colleagues
The essay then makes the interesting move of arguing that, in fact, Jack Sepkoski was not the first to do quantitative palaeontology. The son, a historian, argues that Heinrich Georg Bronn in the 19th century was collecting similar data on paper and visualizing it (see spindle diagram above), but his approach didn’t take.
This raises the question of why Sepkoski senior’s data-driven approach changed palaeontology while Bronn’s didn’t. Sepkoski junior’s answer is a combination of changes. First, that palaeontology became more receptive to ideas like Stephen Jay Gould’s “punctuated equillibrium” that challenged Darwin’s gradualist view. Second, that culture has become more open to data-driven approaches and the interpretation visualizations needed to grasp such approaches.
The essay concludes by warning us about the dangers of believing data black boxes and visualizations that you can’t unpack.
Yet in our own time, it’s taken for granted that the best way of understanding large, complex phenomena often involves ‘crunching’ the numbers via computers, and projecting the results as visual summaries.
That’s not a bad thing, but it poses some challenges. In many scientific fields, from genetics to economics to palaeobiology, a kind of implicit trust is placed in the images and the algorithms that produce them. Often viewers have almost no idea how they were constructed.
This leads me to ask about the warning as gesture. This is a gesture we see more and more, especially about the ethics of big data and about artificial intelligence. No thoughtful person, including myself, has not warned people about the dangers of these apparently new technologies. But what good are these warnings?
Johanna Drucker in Graphesis proposes what to my mind is a much healthier approach to the dangers and opportunities of visualization. She does what humanists do, she asks us to think of visualization as interpretation. If you think of it this way than it is no more or less dangerous than any other interpretation. And, we have the tools to think-through visualization. She shows us how to look at the genealogy of different types of visualization. She shows us how all visualizations are interpretations and therefore need to be read. She frees us to be interpretative with our visualizations. If they are made by the visualizer and are not given by the data as by Moses coming down the mountain, then they are an art that we can play with and through. This is what the 3DH project is about.
The problem isn’t that poor children don’t have access to computers. It’s that they spend too much time in front of them.
The New York Times has an important Opinion about America’s Real Digital Divide by Naomi S. Riley from Feb. 11, 2018. She argues that TV and video game screen time is bad for children and there is no evidence that computer screen time is helpful. The digital divide is not one of access to screens but one of attitude and education on screen time.
But no one is telling poorer parents about the dangers of screen time. For instance, according to a 2012 Pew survey, just 39 percent of parents with incomes of less than $30,000 a year say they are “very concerned” about this issue, compared with about six in 10 parents in higher-earning households.