Tag: science

analysis of content vs. analysis of distribution of media

A theme that keeps coming up for me in work and conversation lately is the difference between analysis of the content of media and analysis of the distribution of media.

Analysis of content looks for the tropes, motifs, psychological intentions, unconscious historical influences, etc. of the media. Over Thanksgiving a friend of mine was arguing that the Scorpions were a dog whistle to white listeners because that band made a deliberate move to distance themselves from influence of black music on rock. Contrast this with Def Leppard. He reached this conclusion based by listening carefully to the beats and contextualizing them in historical conversations that were happening at the time.

Analysis of distribution looks at information flow and the systemic channels that shape it. How did the telegraph change patterns of communication? How did television? Radio? The Internet? Google? Facebook? Twitter? Ello? Who is paying for the distribution of this media? How far does the signal reach?

Each of these views is incomplete. Just as data underdetermines hypotheses, media underdetermines its interpretation. In both cases, a more complete understanding of the etiology of the data/media is needed to select between competing hypotheses. We can’t truly understand content unless we understand the channels through which it passes.

Analysis of distribution is more difficult than analysis of content because distribution is less visible. It is much easier to possess and study data/media than it is to possess and study the means of distribution. The means of distribution are a kind of capital. Those that study it from the outside must work hard to get anything better than a superficial view of it. Those on the inside work hard to get a deep view of it that stays up to date.

Part of the difficulty of analysis of distribution is that the system of distribution depends on the totality of information passing through it. Communication involves the dynamic engagement of both speakers and an audience. So a complete analysis of distribution must include an analysis of content for every piece of implicated content.

One thing that makes the content analysis necessary for analysis of distribution more difficult than what passes for content analysis simpliciter is that the former needs to take into account incorrect interpretation. Suppose you were trying to understand the popularity of Fascist propaganda in pre-WWII Germany and were interested in how the state owned the mass media channels. You could initially base your theory simply on how people were getting bombarded by the same information all the time. But you would at some point need to consider how the audience was reacting. Was it stirring feelings of patriotic national identity? Did they experience communal feelings with others sharing similar opinions? As propaganda provided interpretations of Shakespeare saying he was secretly a German and denunciation of other works as “degenerate art”, did the audience believe this content analysis? Did their belief in the propaganda allow them to continue to endorse the systems of distribution in which they took part?

This shows how the question of how media is interpreted is a political battle fought by many. Nobody fighting these battles is an impartial scientist. Since one gets an understanding of the means of distribution through impartial science, and since this understanding of the means of distribution is necessary for correct content analysis, we can dismiss most content analysis as speculative garbage, from a scientific perspective. What this kind of content analysis is instead is art. It can be really beautiful and important art.

On the other hand, since distribution analysis depends on the analysis of every piece of implicated content, distribution analysis is ultimately hopeless without automated methods for content analysis. This is one reason why machine learning techniques for analyzing text, images, and video are such a hot research area. While the techniques for optimizing supply chain logistics (for example) are rather old, the automated processing of media is a more subtle problem precisely because it involves the interpretation and reinterpretation by finite subjects.

By “finite subject” here I mean subjects that are inescapably limited by the boundaries of their own perspective. These limits are what makes their interpretation possible and also what makes their interpretation incomplete.

reflective data science: technical, practical and emancipatory interests?

As Cathryn Carson is currently my boss at Berkeley’s D-Lab, it seems like it behooves me to read her papers. Thankfully, we share an interest in Habermasian epistemology. Today I read her “Science as instrumental reason: Heidegger, Habermas, Heisenberg.”

Though I can barely do justice to the paper, I’ll try to summarize: it grapples with the history of how science became constructed as purely instrumental project (a mode of inquiry that perfects means without specifying particular ends) through the interactions between Heisenberg, the premier theoretical physicist of Germany at his time, and Heidegger, the great philosopher, and then later the response to Heidegger by Habermas.

Heisenberg, most famous perhaps for the Heisenberg Uncertainty Principle, was himself reflective on the role of the scientist within science, and identified the limits of the subject and measurement within physics. But far from surpassing an older metaphysical idea of the subject-object divide, this only entrenched the scientist further, according to Heidegger. This is because scientist qua scientist never encounters the world in a way that is not tied up in the scientific, technical mode and so elludes pure being. While that may simply mean that pure being is left for philosophers and scientists are allowed to go on with their instrumental project, this mode of inquiry becomes insufficient when scientists were called on to comment on nuclear proliferation policy.

Such policy decisions are questions of praxis, or practical action in the human (as opposed to natural) world. Habermas was concerned with the hermeneutic epistemology of praxis, as well as the critical epistemology of emancipation, which are more the purview of the social sciences. Habermas tends to segment these modes of inquiry from each other, without (as far as I’ve encountered so far) anticipating a synthesis.

In data science, we see the broadly positivist, statistical, analytic treatment of social data. In its commercial applications to sell ads or conduct high-speed trading, we could say on a first pass that the science serves the technical human interest: prediction and control for some unspecified end. But that would be misleading. The breadth of methodological options available to the data scientist mean the the methods are often very closely tailored to the particular ends and conditions of the project. Data science as a method is an instrument. But the results of commercial data science are by and large not nomological (identifying laws of human behavior), but rather an immediately applied idiography. Or, more than an applied idiography, data science provides a probabilistic profile of its diverse subjects–an electron cloud of possibilities that the commercial data scientist uses to steer behavior en masse.

Of course, the uncertainty principle applies here as well: the human subject reacts to being measured, has the potential to change direction when they see that they are being targetted with this ad or that.

Further complicating the picture is that the application of ‘social technology’ of commercially driven data science is praxis, albeit in an apolitical sense. Enmeshed in a thick and complex technological web, nevertheless showing an ad and having it be clicked on is a move in the game of social relations. It is a handshake between cyborgs. And so even commercial data science must engage in hermeneutics, if Habermas is correct. Natural language processing provides the uncomfortable edge case here: can we have a technology that accomplishes hermeneutics for us? Apparently so, if a machine can identify somebody’s interest in a product or service from their linguistic output.

Though jarring, this is easier to cope with intellectually if we see the hermeneutic agent as a socio-technical system, as opposed to a purely technical system. Cyborg praxis will includes statistical/technical systems made of wires and silicon, just as meatier praxis includes statistical/technical systems made of proteins and cartilege.

But what of emancipation? This is the least likely human interest to be advanced by commercial interests. If I’ve got my bearings right, the emancipatory interest in the (social) sciences comes from the critical theory tradition, perhaps best exemplified in German thought by the Frankfurt School. One is meant to be emancipated by such inquiry from the power of the capitalist state. What would it mean for there to be an emancipatory data science?

I was recently asked out of the blue in an email whether there were any organizations using machine learning and predictive analytics towards social justice interests. I was ashamed to say I didn’t know of any organizations doing that kind of work. It is hard to imagine what an emancipatory data science would look like. An education or communication about data scientific techniques might be emancipatory (I was trying to accomplish something like this with Why Weird Twitter, for what its worth), but that was a qualitative study, not a data scientific one.

Taking our cue from above, an emancipatory data science would have to use data science methods towards the human interest of emancipation. For this, we would need to use the methods to understand the conditions of power and dependency that bind us. Difficult as an individual, it’s possible that these techniques could be used to greater effect by an emancipatory sociotechnical organization. Such an organization would need to be concerned with its own autonomy as well as the autonomy of others.

The closest thing I can imagine to such a sociotechnical system is what Kelty describes as the recursive public: the loose coallition of open source developers, open access researchers, and others concerned with transforming their social, economic, and technical conditions for emancipatory ends. Happily, the D-Lab’s technical infrastructure team appears to be populated entirely by citizens of the recursive public. Though this is naturally a matter of minor controversy within the lab (its hard to convince folks who haven’t directly experienced the emancipatory potential of the movement of its value), I’m glad that it stands on more or less robust historical grounds. While the course I am co-teaching on Open Collaboration and Peer Production will likely not get into critical theory, I expect that the exposure to more emancipated communities of praxis will make something click.

What I’m going for, personally, is a synthetic science that is at once technical and engaged in emancipatory praxis.

dreams of reason

Begin insomniac academic blogging:

Dave Lester has explained his strategy in graduate school as “living agily”, a reference to agile software development.

In trying to navigate the academic world, I find myself sniffing the air through conversations, email exchanges, tweets. Since this feels like part of my full time job, I have been approaching the task with gusto and believe I am learning rapidly.

Intellectual fashions shift quickly. A year ago I first heard the term “digital humanities”. At the time, it appeared to be controversial but on the rise. Now, it seems like something people are either disillusioned with or pissed about. (What’s this based on? A couple conversations this week, a few tweets. Is that sufficient grounds to reify a ‘trend’?)

I have no dog in that race yet. I can’t claim to understand what “digital humanities” means. But from what I gather, it represents a serious attempt to approach text in its quantitative/qualitative duality.

It seems that such a research program would: (a) fall short of traditional humanities methods at first, due to the primitive nature of the tools available, (b) become more insightful as the tools develop, and so (c) be both disgusting and threatening to humanities scholars who would prefer that their industry not be disrupted.

I was reminded through an exchange with some Facebook Marxists that Hegel wrote about the relationship between the quantitative and the qualitative. I forget if quantity was a moment in transition to quality, or the other way around, or if they bear some mutual relationship, for Hegel.

I’m both exhausted about and excited that in order to understand the evolution of the environment I’m in, and make strategic choices about how to apply myself, I have to (re?)read some Hegel. I believe the relevant sections are this and this from his Science of Logic.

This just in! Information about why people are outraged by digital humanities!

There we have it. Confirmation that outrage at digital humanities is against the funding of research based on the assumption that “that formal characteristics of a text may also be of importance in calling a fictional text literary or non-literary, and good or bad”–i.e., that some aspects of literary quality may be reducible to quantitative properties of the text.

A lot of progress has been made in psychology by assuming that psychological properties–manifestly qualitative–supervene on quantitatively articulated properties of physical reality. The study of neurocomputation, for example, depends on this. This leads to all sorts of cool new technology, like prosthetic limbs and hearing aids and combat drones controlled by dreaming children (potentially).

So, is it safe to say that if you’re against digital humanities, you are against the unremitting march of technical progress? I suppose I could see why one would be, but I think that’s something we have to take a gamble on, steering it as we go.

In related news, I am getting a lot out of my course on statistical learning theory. Looking up something I wanted to include in this post just now about what I’ve been learning, I found this funny picture:

One thing that’s great about this picture is how it makes explicit how, in a model of the mind adopted by statistical cognitive science theorists, The World is understood by us through a mentally internal Estimator whose parameters are strictly speaking quantitative. They are quantitative because they are posited to instantiate certain algorithms, such as those derived by statistical learning theorists. These algorithmic functions presumably supervene on a neurocomputational substrate.

But that’s a digression. What I wanted to say is how exciting belief propagation algorithms for computing marginal probabilities on probabilistic graphical models are!

What’s exciting about them is the promise they hold for the convergence of opinion onto correct belief based on a simple algorithm. Each node in a network of variables listens to all of its neighbors. Occasionally (on a schedule whose parameters are free for optimization to context) the node will synthesize the state of all of its neighbors except one, then push that “message” to its neighbor, who is listening…

…and so on, recursively. This algorithm has nice mathematically guaranteed convergence properties when the underlying graph has no cycles. Meaning, the algorithm finds the truth about the marginal probabilities of the nodes in a guaranteed amount of time.

It also has some nice empirically determined properties when the underlying graph has cycles.

The metaphor is loose, at this point. If I could dream my thesis into being at this moment, it would be a theoretical reduction of discourse on the internet (as a special case of discourse in general) to belief propagation on probabilistic graphical models. Ideally, it would have to account for adversarial agents within the system (i.e. it would have to be analyzed for its security properties), and support design recommendations for technology that catalyzed the process.

I think it’s possible. Not done alone, of course, but what projects are ever really undertaken alone?

Would it be good for the world? I’m not sure. Maybe if done right.

digital qualities: some meditations on methodology

Text is a kind of data that is both qualitative (interpretable for the qualities it conveys) and qualitative (characterized by certain amounts of certain abstract tokens arranged in a specific order).

Statistical learning techniques are able to extract qualitative distinctions from quantitative data, through clustering processes for example. Non-parametric statistical methods allow qualitative distinctions to be extracted from quantitative data without specifying particular structure or features up front.

Many cognitive scientists and computational neuroscientists believe that this is more or less how perception works. The neurons in our eyes (for example) provide a certain kind of data to downstream neurons, which activate according to quantifiable regularities in neuron activation. A qualitative difference that we perceive is due to a statistical aggregation of these inputs in the context of a prior, physically definite, field of neural connectivity.

A source of debate in the social sciences is the relationship between qualitative and quantitative research methods. As heirs to the methods of harder sciences whose success is indubitable, quantitative research is often assumed to be credible up to the profound limits of its method. A significant amount of ink has been spilled distinguishing qualitative research from quantitative research and justifying it in the face of skeptical quantitative types.

Qualitative researchers, as a rule, work with text. This is trivially true due to the fact that a limiting condition of qualitative research appears to be the creation of a document explicating the research conclusions. But if we are to believe several instructional manuals on qualitative research, then the work of an e.g. ethnographer involves jottings, field notes, interview transcripts, media transcripts, coding of notes, axial coding of notes, theoretical coding of notes, or, more broadly, the noting of narratives (often written down), the interpreting of text, a hermeneutic exposition of hermeneutic expositions ad infinitum down an endless semiotic staircase.

Computer assisted qualitative data analysis software passes the Wikipedia test for “does it exist”.

Data processed by computers is necessarily quantitative. Hence, qualitative data is necessarily quantitative. This is unsurprising, since so much qualitative data is text. (See above).

We might ask: what makes the work qualitative researchers do qualitative as opposed to quantitative, if the data they work with with quantitative? We could answer: it’s their conclusions that are qualitative.

But so are the conclusions of a quantitative researcher. A hypothesis is, generally speaking, a qualitative assessment, that is then operationalized into a prediction whose correspondence with data can be captured quantitatively through a statistical model. The statistical apparatus is meant to guide our expectations of the generalizability of results.

Maybe the qualitative researcher isn’t trying to get generalized results. Maybe they are just reporting a specific instance. Maybe generalizations are up to the individual interpreter. Maybe social scientific research can only apply and elaborate on an ideal type, tell a good story. All further insight is beyond the purview of the social sciences.

Hey, I don’t mean to be insensitive about this, but I’ve got two practical considerations: first, do you expect anyone to pay you for research that is literally ungeneralizable? That has no predictive or informative impact on the future? Second, if you believe that, aren’t you basically giving up all ground on social prediction to economists? Do you really want that?

Then there’s the mixed methods researcher. Or, the researcher who in principle admits that mixed methods are possible. Sure, the quantitative folks are cool. We’d just rather be interviewing people because we don’t like math.

That’s alright. Math isn’t for everybody. It would be nice if computers did it for us. (See above)

What some people say is: qualitative research generates hypotheses, quantitative research tests hypotheses.

Listen: that is totally buying into the hegemony of quantitative methods by relegating qualitative methods to an auxiliary role with no authority.

Let’s accept that hegemony as an assumption for a second, just to see where it goes. All authority comes from a quantitatively supported judgment. This includes the assessment of the qualitative researcher.

We might ask, “Where are the missing scientists?” about qualitative research, if it is to have any authority at all, even in its auxiliary role.

What would Bruno Latour do?

We could locate the missing scientists in the technological artifacts that qualitative researchers engage with. The missing scientists may lie within the computer assisted qualitative data analysis software, which dutifully treats qualitative data as numbers, and tests the data experimentally and in a controlled way. The user interface is the software’s experimental instrument, through which it elicits “qualitative” judgments from its users. Of course, to the software, the qualitative judgments are quantitative data about the cognitive systems of the software’s users, black boxes that nevertheless have a mysterious regularity to them. The better the coding of the qualitative data, the better the mysteries of the black box users are consolidated into regularities. From the perspective of the computer assisted qualitative data analysis software, the whole world, including its users, is quantitative. By delegating quantitative effort to this software, we conserve the total mass of science in the universe. The missing mass is in the software. Or, maybe, in the visual system of the qualitative researchers, which performs non-parametric statistical inference on the available sensory data as delivered by photo-transmitters in the eye.

I’m sorry. I have to stop. Did you enjoy that? Did my Latourian analysis convince you of the primacy or at least irreducibility of the quantitative element within the social sciences?

I have a confession. Everything I’ve ever read by Latour smells like bullshit to me. If writing that here and now means I will never be employed in a university, then may God have mercy on the soul of academe, because its mind is rotten and its body dissolute. He is obviously a brilliant man but as far as I can tell nothing he writes is true. That said, if you are inclined to disagree, I challenge you to refute my Latourian analysis above, else weep before the might of quantification, which will forever dominate the process of inquiry, if not in man, then in our robot overlords and the unconscious neurological processes that prefigure them.

This is all absurd, of course. Simultaneously accepting the hegemony of quantitative methods
and Latourian analysis has provided us with a reductio ad absurdum that compels us to negate some assumptions. If we discard Latourian analysis, then our quantitative “hegemony” dissolves as more and more quantitative work is performed by unthinking technology. All research becomes qualitative, a scholarly consideration of the poetry outputted by our software and instruments.

Nope, that’s not it either. Because somebody is building that software and those instruments, and that requires generalizability of knowledge, which so far qualitative methods have given up a precise claim to.

I’m going to skip some steps and cut to the chase:

I think the quantitative/qualitative distinction in social scientific research, and in research in general, is dumb.

I think researchers should recognize the fungibility of quantity and quality in text and other kinds of data. I think ethnographers and statistical learning theorists should warmly embrace each other and experience the bliss that is finding ones complement.


Academia vs. FOSS: The Good, The Bad, and the Ugly

Mel Chua has been pushing forward on the theme of FOSS culture in academia, and has gotten a lot of wonderful comments, many about why it’s not so simple to just port one culture over to the other. I want to try to compile items from Mel, comments on that post, and a few other sources. The question is: what are the salient differences between FOSS and academia?

I will proceed using the now-standard Spaghetti Western classification schema.

The Good

  • Universities tend to be more proactive about identifying and aiding newcomers that are struggling, as opposed to many FOSS projects that have high failure-and-dropout rates due to poorly designed scaffolding.
  • Academia is much more demographically inclusive. FOSS communities are notoriously imbalanced in terms of gender and race.

The Bad

  • The academic fear of having ones results scooped or stolen results in redundant, secrecy, and lonely effort. FOSS communities get around this by having good systems for attribution of incremental progress.
  • Despite scientific ideals, academic scientific research is getting less reproducible, and therefore less robust, because of closed code and data. FOSS work is often more reproducible (though not if its poorly documented).
  • Closed access academic journals hold many disciplines hostage by holding a monopoly on prestige. This is changing with the push for open access research, but this is still a significant issue. FOSS communities may care about community prestige, but often that prestige comes from community helpfulness or stake in a project. If metrics are used, they are often implicit ones extractable from the code repository itself, like Ohloh. Altmetrics are a solution to this problem.

The Ugly

  • In both FOSS and academia, a community of collaborators needs to form around shared interests and skills. But FOSS has come to exemplify the power of the distributed collaboration towards pragmatic goals. One is judged more by ones contributions than by ones academic pedigree, which means that FOSS does not have as much institutional gatekeeping.
  • Tenure committees look at papers published, not software developed. So there is little incentive for making robust software as part of the research process, however much that might allow reproducibility and encourage collaboration.
  • Since academics are often focused on “the frontier”, they don’t pay much attention to “building blocks”. Academic research culture tends to encourage this because it’s a race for discovery. FOSS regards care of the building blocks as a virtue and rewards the effort with stronger communities built on top of those blocks.
  • One reason for the difference between academia and FOSS is bandwidth. Since publications have page limits and are also the main means of academic communication, one wants to dedicate as much space as possible to juicy results at the expense of process documentation that would aid reproducibility. Since FOSS developed using digital communication tools with fewer constraints, it doesn’t have this problem. But academia doesn’t yet value contributions to this amorphous digital wealth of knowledge.

Have I left anything out?