Tuesday, May 3, 2016
Daniel Allington, Sarah Brouillette, and David Golumbia have published a critique of digital humanities (mostly just digital work in english lit.) in the LARB: Neoliberal Tools (and Archives): A Political History of Digital Humanities. Here's the opening paragraph:
Advocates position Digital Humanities as a corrective to the “traditional” and outmoded approaches to literary study that supposedly plague English departments. Like much of the rhetoric surrounding Silicon Valley today, this discourse sees technological innovation as an end in itself and equates the development of disruptive business models with political progress. Yet despite the aggressive promotion of Digital Humanities as a radical insurgency, its institutional success has for the most part involved the displacement of politically progressive humanities scholarship and activism in favor of the manufacture of digital tools and archives. Advocates characterize the development of such tools as revolutionary and claim that other literary scholars fail to see their political import due to fear or ignorance of technology. But the unparalleled level of material support that Digital Humanities has received suggests that its most significant contribution to academic politics may lie in its (perhaps unintentional) facilitation of the neoliberal takeover of the university.
It has provided 30 comments so far, most of them critical. It also provoked Alan Liu to respond with a twitter stream which he then Storified: On Digital Humanities and "Critique". He his not happy, referring to the article's "character assassination of the still evolving field of DH...as an attempt at a last word foreclosing the critical potential of DH". Liu also posted drafts from a book he's working on:
“Drafts for Against the Cultural Singularity (book in progress.” Alan Liu, 2 May 2016. http://liu.english.ucsb.edu/drafts-for-against-the-cultural-singularity
I'm about to read through the drafts. Here's Liu's opening paragraph:
My aim in this book is to make a strategic intervention in the development of the digital humanities. Following up on my 2012 essay, “Where is Cultural Criticism in the Digital Humanities?”, I call for digital humanities research and development informed by, and able to influence, the way scholarship, teaching, administration, support services, labor practices, and even development and investment strategies in higher education intersect with society, where a significant channel of the intersection between the academy and other social sectors, at once symbolic and instrumental, consists in shared but contested information-technology infrastructures. I first lay out in the book a methodological framework for understanding how the digital humanities can develop a mode of critical infrastructure studies. I then offer a prospectus for the kinds of infrastructure (not only research “cyberinfrastructures,” as they have been called) whose development the digital humanities might help create or guide. And I close with thoughts on how the digital humanities can contribute to ameliorating the very idea of “development”–technological, socioeconomic, and cultural–today.
This is a paper that is apparently unpublished. Robin Hanson has uploaded it to Research Gate.
I defend the relevance of fiction for social science investigation. Novels can be useful for making some economic approaches -- such as behavioral economics or signaling theory -- more plausible. Novels are more like models than is commonly believed. Some novels present verbal models of reality. I interpret other novels as a kind of simulation, akin to how simulations are used in economics. Economics can, and has, profited from the insights contained in novels. Nonetheless, while novels and models lie along a common spectrum, they differ in many particulars. I attempt a partial account of why we sometimes look to models for understanding, and other times look to novels.
[accessed May 3, 2016].
From the article itself:
An investigation of novels and models also may help us better understand how the public thinks about economic issues. Economists typically use formal models to think about the world. We cannot help but notice that most members of the general public do not appear to think very scientifically about policy, in the sense of deferring to the established expert bodies of knowledge. Instead most citizens are heavily influenced by stories, movies, and popular culture. They think in terms of narrative, often false narrative, and spend little time learning economics. Economists naturally wonder whether citizens and voters spend too much time thinking in terms of stories and not enough time thinking in terms of models.
Novels enrich our sense of people's motivations:
A familiarity with novels increases the plausibility of behavioral economics. Most characters in novels have complex motivations and show a variety of behavioral quirks. For instance, Flaubert's characters often exhibit a "grass is always greener" approach to romantic choice, rather than rationally assessing their current and future prospects. Madame Bovary seems to want every man but the one, her husband, who adores her. The lead characters in Bronte's Wuthering Heights (Heathcliff and Catherine) consider themselves connected by a sense of common fate and destiny, and they pursue disastrous courses of action. Captain Ahab, from Melville's Moby Dick, is obsessed with taking his revenge against the white whale, which eventually leads to his death.Utility maximization may describe the behavior of these characters ex post, but it does not help us understand or predict their behavior very much. Instead their behavior appears best described in terms of complex psychological mechanisms. ...The standard criticism of behavioral economics is that it offers too many varying accounts of human behavior, with no unified framework or no ability to offer useful exante predictions. A reader of novels, who is used to complex portraits of multi-faceted characters, is less likely to find such a criticism persuasive. Such a reader is less likely to see simplicity as an explanatory virtue, and is less likely to look for unified accounts of complex social phenomena.
Sunday, May 1, 2016
I took this photo on April 24 of this year:
It's a graffiti production by the AIDS (= Alone in Deep Space | America is Dying Slowly) crew in Jersey City. As you can see it's been treated harshly by the weather, with the right half badly washed out. But no one's gone over it, which typically happens with old graffiti. Yet other walls in that area – there are over a dozen in close proximity – have been gone over many times. What's the difference?
Perhaps it is that this wall is just a bit more exposed than some of the others, and so you're a bit more likely to get caught. But I think mostly it's a matter of respect for the writers and the crew.
Here's what that wall looked like the first time I flicked it, on Nov. 28, 2006:
Saturday, April 30, 2016
We know that Jerry Seinfeld is an admirer of Obama's comedy skills; that's why he had him on his cars and comedy coffee klatch.. Now a former White House speech writer, David Litt, has written at NYTimes opinion piece about the President's comedy skills.
Part of what makes any presidential joke funny is the fact that the president is telling a joke. But this president has a talent for comedy — an impressive sense of timing and audience. His administration combined that talent with an understanding of a changing media landscape and the emergence of viral videos. Jokes became a real tool to move his agenda forward.
But they didn't really tap Obama's comedy skills until after the somewhat rocky roll-out of Obamacare:
By March 2014, the health care exchanges were finally working, but most young people didn’t seem to know that. Not enough of them were signing up. One solution, at least in part, was for President Obama to plug the site on the comedian Zach Galifianakis’s online talk show “Between Two Ferns.” The commander in chief sat between two ferns and listened as the comedian asked him, “What’s it like to be the last black president?” before they got around to talking health care.The day the “Ferns” video appeared online it was viewed by 11 million people, and traffic to HealthCare.gov spiked 40 percent. Of course that video isn’t the only reason the administration can now report that 20 million people are enrolled in insurance through the Affordable Care Act. But it certainly helped get the word out.
But what struck me about the article came up front. Pitt was writing about Luther, Obama's "anger translator":
Each time Mr. Key, as the anger translator, began a new manic tirade, the president burst out laughing. Already dressed in his tuxedo for the evening, he glanced toward us, his staff, huddled in a corner of the room.“I’ve got to hold it together,” Mr. Obama said. He said it again backstage a few hours later, this time using a comedy term for laughing in the middle of a scene. “I have to make sure I don’t break.”
Think about that for a minute. The comedian's job is to get the audience to laugh. But the comedian cannot, absolutely cannot, laugh at his own jokes. How does that work? That's a tricky bit of psychology.
* * * * *
The President and his anger translator:
Friday, April 29, 2016
Wikipedia is one of the (potentially) great social experiments of our time. A large self-organizing community built from the bottom up. Ah! freedom! And what got built? According to an exhaustive study that examined the sight, which records every action taken in its construction an maintenance, what got built is a standard corporate hierarchy. Writing in Gizmodo, Jennifer Ouellette reports:
One of their most striking findings is that, even on Wikipedia, the so-called “Iron Law of Oligarchy”—a.k.a. rule by an elite few—holds sway. German sociologist Robert Michels coined the phrase in 1911, while studying Italian political parties, and it led him to conclude that democracy was doomed. “He ended up working for Mussolini,” said DeDeo, who naturally learned about Michels via Wikipedia.“You start with a decentralized democratic system, but over time you get the emergence of a leadership class with privileged access to information and social networks,” DeDeo explained. “Their interests begin to diverge from the rest of the group. They no longer have the same needs and goals. So not only do they come to gain the most power within the system, but they may use it in ways that conflict with the needs of everybody else.”He and Heaberlin found that the same is true of Wikipedia. The core norms governing the community were created by roughly 100 users—but the community now numbers about 30,000.
There goes the neighborhood! But then, how could it have been otherwise? Top-down hierarchies are all the most of us know, right?
* * * * *
Here's the study:
Future Internet 2016, 8(2), 14; doi:10.3390/fi8020014
The Evolution of Wikipedia’s Norm Network
Bradi Heaberlin and Simon DeDeo
Abstract: Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. One exception is that of the network of norms that sustain the online Wikipedia community. We study the fifteen-year evolution of this network using the interconnected set of pages that establish, describe, and interpret the community’s norms. Despite Wikipedia’s reputation for ad hoc governance, we find that its normative evolution is highly conservative. The earliest users create norms that both dominate the network and persist over time. These core norms govern both content and interpersonal interactions using abstract principles such as neutrality, verifiability, and assume good faith. As the network grows, norm neighborhoods decouple topologically from each other, while increasing in semantic coherence. Taken together, these results suggest that the evolution of Wikipedia’s norm network is akin to bureaucratic systems that predate the information age.
And a related study:
Intellectual interchanges in the history of the massive online open-editing encyclopedia, Wikipedia
Jinhyuk Yun (윤진혁), Sang Hoon Lee (이상훈), and Hawoong Jeong (정하웅)
Phys. Rev. E 93, 012307 – Published 22 January 2016
Abstract: Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, without consideration of real time. In this work, we probe the formation of such collective intelligence through a systematic analysis using the entire history of 34534110 English Wikipedia articles, between 2001 and 2014. From this massive data set, we observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.
Wednesday, April 27, 2016
A Continuous Semantic Space Describes
the Representation of Thousands of Object and Action Categories across the Human Brain
Alexander G. Huth,1 Shinji Nishimoto,1 An T. Vu,2 and Jack L. Gallant1,2,3,* 1Helen Wills Neuroscience Institute
2Program in Bioengineering
3Department of Psychology
the Representation of Thousands of Object and Action Categories across the Human Brain
Alexander G. Huth,1 Shinji Nishimoto,1 An T. Vu,2 and Jack L. Gallant1,2,3,* 1Helen Wills Neuroscience Institute
2Program in Bioengineering
3Department of Psychology
University of California, Berkeley, Berkeley, CA 94720, USA *Correspondence: firstname.lastname@example.org
Humans can see and name thousands of distinct object and action categories, so it is unlikely that each category is represented in a distinct brain area. A more efficient scheme would be to represent categories as locations in a continuous semantic space mapped smoothly across the cortical surface. To search for such a space, we used fMRI to measure human brain activity evoked by natural movies. We then used voxelwise models to examine the cortical representation of 1,705 object and action categories. The first few dimensions of the underlying semantic space were recovered from the fit models by prin- cipal components analysis. Projection of the recov- ered semantic space onto cortical flat maps shows that semantic selectivity is organized into smooth gradients that cover much of visual and nonvisual cortex. Furthermore, both the recovered semantic space and the cortical organization of the space are shared across different individuals.
Some remarks by the lead author, Alexander Huth:
* * * * *
Some remarks by the lead author, Alexander Huth:
Back in 2012 I wrote a paper about the cortical representation of visual semantic categories. I showed that pretty much all of the higher visual cortex is semantically selective, and argued that this representation is better understood as gradients of selectivity across the cortex than as distinct areas. I also made a video that explains the paper, and there's a nice FAQ on our lab website. I also made a nify online viewerfor that dataset.
Tuesday, April 26, 2016
I've been thinking about some remarks Moretti made about the digital humanities in a recent interview. Among other things he suggested that the results of computational criticism have so far been disappointing. But he also held up Lit Lab Pamphlet #4 as an example of "an intelligence that takes the form of writing a script, but in the writing of the script there is also the beginning of a concept, very often not expressed as a concept, but that you can see that it was there from the results that the coding produces." Here's what I wrote about that pamphlet back in October of 2012.I’ve just looked at a pamphlet from Stanford’s Literary Lab: Ryan Heuser and Long Le-Khac, A Quantitative Literary History Of 2,958 Nineteenth-Century British Novels: The Semantic Cohort Method (68 page PDF), May 2012. I’ve not read it in detail, but only blitzed my way through, looking for the good parts. Well, not even all of those. I was just looking to get a sense of what’s going on.
Which I did. And I like it. THIS is the sort of work I want to see from ‘digital humanities.’ Not the only sort, but it’s one of the things we can do with ‘big data’ and pretty much only do with big data. If traditional humanists can’t see value in this kind of work, well, then forget about them.
First I’ll give you the abstract, then I’ll quote a bunch and make some comments.
The nineteenth century in Britain saw tumultuous changes that reshaped the fabric of society and altered the course of modernization. It also saw the rise of the novel to the height of its cultural power as the most important literary form of the period. This paper reports on a long-term experiment in tracing such macroscopic changes in the novel during this crucial period. Specifically, we present findings on two interrelated transformations in novelistic language that reveal a systemic concretization in language and fundamental change in the social spaces of the novel. We show how these shifts have consequences for setting, characterization, and narration as well as implications for the responsiveness of the novel to the dramatic changes in British society.This paper has a second strand as well. This project was simultaneously an experiment in developing quantitative and computational methods for tracing changes in literary language. We wanted to see how far quantifiable features such as word usage could be pushed toward the investigation of literary history. Could we leverage quantitative methods in ways that respect the nuance and complexity we value in the humanities? To this end, we present a second set of results, the techniques and methodological lessons gained in the course of designing and running this project.
Sunday, April 24, 2016
Melissa Dinsman interviews Laura Mandell in the LARB:
Another concern that has come up deals with public intellectualism, which many scholars and journalists alike have described as being in decline (for example, Nicholas Kristof’s New York Times essay last year). What role, if any, do you think digital work plays? Could the digital humanities (or the digital in the humanities) be a much-needed bridge between the academy and the public, or is this perhaps expecting too much of a discipline?I have a story to tell about this. I was at the digital humanities conference at Stanford one year and there was a luncheon at which Alan Liu spoke. His talk was a plea to have the digital humanities help save the humanities by broadcasting humanities work — in other words, making it public. It was a deeply moving talk. But to her credit, Julia Flanders stood up and said something along the lines of, “We don’t want to save the humanities as they are traditionally constituted.” And she is right. There are institutional problems with the humanities that need to be confronted and those same humanities have participated in criticizing the digital humanities. Digital humanists would be shooting themselves in the foot in trying to help the very humanities discipline that discredits us. In many ways Liu wasn’t addressing the correct audience, because he was speaking to those who critique DH and asking that they take that critical drive that is designed to make the world a better place and put it into forging a link with the public — making work publicly available. Habermas has said that the project of Enlightenment is unfinished until we take specialist discourses and bring them back to the public. This has traditionally been seen as a lesser thing to do in the humanities. For Habermas, it is seen as the finishing of an intellectual trajectory. This is a trajectory that we have not yet completed and it is something, I think, the digital humanities can offer.
I like that: “We don’t want to save the humanities as they are traditionally constituted.”
Saturday, April 23, 2016
Philip Ball, in Nautilus: Why Physics Is Not a Discipline. Rather, it's a mode of thinking that knows no disciplinary bounds.
The habit of physicists to praise peers for their ability to see to the “physics of the problem” might sound odd. What else would a physicist do but think about the “physics of the problem?” But therein lies a misunderstanding. What is being articulated here is an ability to look beyond mathematical descriptions or details of this or that interaction, and to work out the underlying concepts involved—often very general ones that can be expressed concisely in non-mathematical, perhaps even colloquial, language. Physics in this sense is not a fixed set of procedures, nor does it alight on a particular class of subject matter. It is a way of thinking about the world: a scheme that organizes cause and effect.This kind of thinking can come from any scientist, whatever his or her academic label. It’s what Jacob and Monod displayed when they saw that feedback processes were the key to genetic regulation, and so forged a link with cybernetics and control theory. It’s what the developmental biologist Hans Meinhardt did in the 1970s when he and his colleague Alfred Gierer unlocked the physics of Turing structures. These are spontaneous patterns that arise in a mathematical model of diffusing chemicals, devised by mathematician Alan Turing in 1952 to account for the generation of form and order in embryos. Meinhardt and Gierer identified the physics underlying Turing’s maths: the interaction between a self-generating “activator” chemical and an ingredient that inhibits its behavior.Once we move past the departmental definition of physics, the walls around other disciplines become more porous, to positive effect. Mayr’s argument that biological agents are motivated by goals in ways that inanimate objects are not was closely tied to a crude interpretation of biological information springing from the view that everything begins with DNA. As Mayr puts it, “there is not a single phenomenon or a single process in the living world which is not controlled by a genetic program contained in the genome.”This “DNA chauvinism,” as it is sometimes now dubbed, leads to the very reductionism and determinism that Mayr wrongly ascribes to physics, and which the physics of biology is undermining. For even if we recognize (as we must) that DNA and genes really are central to the detailed particulars of how life evolves and survives, there’s a need for a broader picture in which information for maintaining life doesn’t just come from a DNA data bank. One of the key issues here is causation: In what directions does information flow? It’s now becoming possible to quantify these questions of causation—and that reveals the deficiencies of a universal bottom-up picture.
Biological systems seem to operate close to critical points, where the system changes from one phase to another:
I have written variously about behavioral mode. Those modes may be considered different phases of mind.By operating close to a critical point, Bialek and Mora said, a system undergoes big fluctuations that give it access to a wide range of different configurations of its components. As a result, Mora says, “being critical may confer the necessary flexibility to deal with complex and unpredictable environments.” What’s more, a near-critical state is extremely responsive to disturbances in the environment, which can send rippling effects throughout the whole system. That can help a biological system to adapt very rapidly to change: A flock of birds or a school of fish can respond very quickly to the approach of a predator, say.Criticality can also provide an information-gathering mechanism. Physicist Amos Maritan at the University of Padova in Italy and coworkers have shown that a critical state in a collection of “cognitive agents”—they could be individual organisms, or neurons, for example—allows the system to “sense” what is going on around it: to encode a kind of ‘internal map’ of its environment and circumstances, rather like a river network encoding a map of the surrounding topography. “Being poised at criticality provides the system with optimal flexibility and evolutionary advantage to cope with and adapt to a highly variable and complex environment,” says Maritan. There’s mounting evidence that brains, gene networks, and flocks of animals really are organized this way. Criticality may be everywhere.
Thursday, April 21, 2016
Scott Alexander has some interesting observations about Donald Trump that he makes by discussing Trump's book, The Art of the Deal. Here's his conclusion about what Trump does as a developer:
As best I can tell, the developer’s job is coordination. This often means blatant lies. The usual process goes like this: the bank would be happy to lend you the money as long as you have guaranteed renters. The renters would be happy to sign up as long as you show them a design. The architect would be happy to design the building as long as you tell them what the government’s allowing. The government would be happy to give you your permit as long as you have a construction company lined up. And the construction company would be happy to sign on with you as long as you have the money from the bank in your pocket. Or some kind of complicated multi-step catch-22 like that. The solution – or at least Trump’s solution – is to tell everybody that all the other players have agreed and the deal is completely done except for their signature. The trick is to lie to the right people in the right order, so that by the time somebody checks to see whether they’ve been conned, you actually do have the signatures you told them that you had. The whole thing sounds very stressful.
And now we get to Trump the politician:
Maybe I’m imagining things, but I feel like this explains a lot about his presidential campaign. People ask him something like “How would you fix Medicare?”, and he gives some vapid answer like “There are tremendous problems with Medicare, but I’m going to hire the best people. I know all of the best doctors and health care executives, and we’re going to cut some amazing deals and have the best Medicare in the world.” And yeah, he did say in his business tips that you should change the frame to avoid being negative to reporters. But this isn’t a negative or a gotcha question. At some point you’d expect Trump to do his homework and get some kind of Medicare plan or other. Instead he just goes off on the same few tangents. This thing about hiring the best people, for example, seems almost like an obsession in the book. But it works for him. [...]These strategies have always worked for him before, and floating off into some intellectual ideal-system-design effort has never worked for him before. So when he says that he’s going to solve Medicare by hiring great managers and knowing all the right people, I don’t think this is some vapid way of avoiding the question. I think it’s the honest output of a mind that works very differently from mine. I’ve been designing ideal systems of government for the heck of it ever since I was old enough to realize what a government was. Trump is at serious risk of actually taking over a government, and such design still doesn’t appeal to him. The best he can do is say that other people are bad at governing, but he’s going to be good at governing, on account of his deal-making skill. I think he honestly believes this. It makes perfect sense in real estate, where some people are good businesspeople, others are bad businesspeople, and the goal is to game the system rather than change it. But in politics, it’s easy to interpret as authoritarianism – “Forget about policy issues, I’m just going to steamroll through this whole thing by being personally strong and talented.”
The world is taken as a given. It contains deals. Some people make the deals well, and they are winners. Other people make the deals poorly, and they are losers. Trump does not need more than this.
Victor Mair has a fascinating post on this topic over at Language Log, with many interesting comments. He begins:
I'm sitting in the San Francisco International Airport waiting for my flight to Taipei. The guy next to me is happily chattering away on his cell phone to someone (or some people) at the other end of the "line". What is curious is that one moment he is speaking in Taiwanese, the next moment in Japanese, then English, and then Mandarin.I don't know whether it is proper to call this "code switching", because he is speaking each of these languages in whole sentences or even blocks of sentences.He does not speak the languages with equal fluency, but they all sound natural and do not require great effort on his part to produce. The man's first language seems to be Taiwanese, then comes Japanese (with a Chinese accent), English (with a multilingual accent), and Taiwan-style Mandarin.
What's going on? That is, who's on the other end of the conversation? Mair has a speculation of his own and others offer suggestions.
In the comments, for example, Gene Andersen:
As one who is incapable of being very good even at English, let alone anything else, it is always an experience to watch somebody like Lothar von Falkenhausen switch from perfectly polished English to French to German to Japanese to Chinese at a meeting without missing a beat. But the amazing linguists are the people from southern India–they grow up having to know English, Hindi and their usual language, and they generally wind up knowing all the Dravidian languages (which are close), and with that background they can learn anything. We had a Telugu-speaking temp for a while–she started chattering away in Bangali with a colleague from Bengal–I asked her if that was her fifth language or what, and she said "My tenth."
Michael c. Dunn:
I once knew a maitre d' in Cairo in the days when the Russians were still around. he was Armenian and grew up knowing Armenian, Russian, Arabic, and maybe Turkish, and had acquired excellent English and at least conversational French and German. That may not have been all. Also a couple I know: the husband grew up speaking English but knew Brazilian Portuguese, learned Persian in the Peace Corps and Arabic studying abroad, then married a Puerto Rican who was studying Italian literature. I've been at gatherings where there was extensive code-switching.
I worked with a guy who from the Netherlands at an American company. He spoke perfect American English with only the faintest trace of an accent. He lived in Barcelona and thus I assume he spoke Spanish though I never heard it myself. His wife is German and they spoke German in their home.I once met up with him at Schipol Airport to do some business nearby. He had to struggle for a minute to reset his brain to speak his native language!It's really sad that so many of us Americans are monolingual.