Harry Potter and the Philosopher’s DIKW.

DIKW= Data. Information. Knowledge. Wisdom. DIKW!

That’s the way things flow. Or more specifically, that’s the way “the knowledge pyramid” says they flow.

From data we gather information we develop into knowledge which leads to wisdom.

Apparently.

But is it really that straightforward? Are our thought processes that streamlined, hierarchical and, let’s face it, uncomplicated? Or is DIKW simply a nice sounding but somewhat reductive anagram to be used when waxing lyrical about the philosophy of knowledge, information systems, information management, or pedagogy?

I for one am not all that convinced by DIKW. And I’m not the only one: the pyramid is widely criticised. But why? Where and how, exactly, does DIKW misrepresent how we think about and manage data and information? Today we’re going to explore the DIKW pyramid; specifically how exactly “data” gets transformed into “wisdom,” what exactly happens to it, and how a different approach to cleaning or processing that “data” can lead us to come to very different conclusions, and thus to very different states of “wisdom”. And to facilitate this philosophising on the nuances of DIKW and its vulnerability to corruption, I’m going to talk about Harry Potter and the “is Snape good or bad” plot that runs through all seven Harry Potter novels. Because, why not? Specifically I’m going to use Snape’s complexity as a character to highlight DIKW’s shortcomings and in particular how DIKW can be corrupted depending on how the data you collect is processed and interpreted.

As we all know, Snape looks kinda evil, acts kinda evil, hates Harry, and has a pretty dodgy past in which he was aligned with Voldemort, the wizard responsible for Harry’s parents’ deaths. He has a fondness for the “Dark Arts,” and, as head of Slytherin, an unhealthy interest in eugenics and so-called “blood purity” (never a good trait in a person). And he is played to absolute perfection by the unrivaled Alan Rickman, sadly now deceased.

Rowling maintains a near-constant back and forth throughout the series, with the characters forever pursuing the idea that Snape is bad, being thwarted in their pursuit of this idea, or thrown off their suspicions by Dumbledore who always reaffirms his strong faith in Snape. The dampening of any suspicion regarding Snape’s motives generally comes at the conclusion of any given book, only for these suspicions to be re-ignited at the start of the next book and the next adventure.

And just when this continual “is he or isn’t he a bad guy” threatens to get monotonous, with the well-trained reader now six books in and attuned to expect the usual — “Snape’s being shifty, ergo…he must be bad!” / “Nope he’s actually good, Dumbledore says so.” / “Oh okay let’s talk about this again in the next book.” — Rowling bucks our expectations spectacularly, and all of these hints and suspicions about Snape are seemingly verified  in book six, Harry Potter and the Half-Blood Prince, when Snape goes and kills Dumbledore, the one man who trusted and protected him absolutely; a most heinous crime, and one done using “Avada Kedavra,” the unforgivable curse.

Lets take a look at Snape’s first appearance, way back in book one, Harry Potter and the Philosopher’s Stone, or as it’s known in the US, Harry Potter and the Sorcerer’s Stone:Screenshot 2017-06-27 13.33.43.png

    (J. K. Rowling, Harry Potter and the Philosopher’s Stone, Bloomsbury 1999, 126.)

What do we get on Snape here?

He’s unhealthy looking, pale and yellowish. He could probably do with a good shampoo. Oh, and he has a hooked nose.

Now this description is controversial. Snape’s portrait could be considered to be “Jewish-coded” or even anti-Semitic; certainly it can be seen as having uneasy inter-textual chimes with overtly anti-Semitic portraits in classic (and classically anti-Semitic) canonical English-language texts such as Charles Dickens’s Oliver Twist or Shakespeare’s The Merchant of Venice where both Fagin and Shylock respectively (both pictured below) are presented as having the overt hooked noses that were considered characteristic of the so-called “Stage Jew.”

The “Stage Jew” is basically the Jewish equivalent of blackface, a crude form of racial stereotyping that was particularly popular during the Elizabethan period and thereafter. Much like blackface, these racist Jewish stereotypes were not just confined to the realms of theatre and literature, Hitler and the Nazi’s also made full use of racist caricatures in their propaganda.

Most all of this will (thankfully) be lost on a younger audience, but the question remains, does Rowling engage with this sadly all-too familiar visual trope deliberately? Knowing Snape’s story, his full story, as she claims she did all the way back in book one, does she proffer such material with a view to ultimately showing it up to be complete rubbish?

Snape’s appearance screams evil, irrespective of whether you want to connect that with the tradition of the Stage Jew, or with less racially charged narratives that seek to represent a person’s character in or through their appearance. It’s a frequent visual trope in Superhero(ine) movies, for example; the good guys look good, the bad guys look bad. And when the good guy turns bad (as they are wont to do on occasion), their nascent badness is represented visually through some change in their appearance. Again, in relation to the Spiderman 3 poster below, we can ask why the “evil” Spiderman is coded “black,” particularly when Spiderman hails from a country whose law enforcement has a well established track record of subjecting African American males to racial profiling, but we better stay on topic.

To return to Snape, to his appearance, and to how our reading of his appearance can change once we get to the crux of his character: Certainly Snape’s character leads us to question many of the facets of other peoples’ appearances we read and take for granted, their demeanour, their silences, their unreadability, their appearance, their complexity. Snape looks bad, but he is not bad, he is good. We have misread him and are guilty of superficially associating his morality with his appearance. This is not to say that readers of Harry Potter are guilty of racially profiling Snape, not at all, but simply that this tendency to create a link between appearance and morality has a long history in English literature, and one that unfortunately happens to have rather unpleasant racist roots that contemporary readers may not be aware of. This is just one small part of why Rowling is such a good author, and why the Harry Potter books are so rich and rewarding for growing minds in particular. But it’s also an example of how the DIKW pyramid can be drastically altered or corrupted depending on how you read the data at the bottom; the data being the material that proffers the opportunity to reach a state of wisdom regarding that particular material or phenomenon.

In other words, even if you’d only seen a very small selection of Marvel superhero movies, you have been trained to seek out information on a person’s moral character based off of their appearance, you may have taken facets of Snape’s malevolent appearance as “data” on Snape. This “data” would allow you to garner “information” on his character, because most figures that look like this in literature or superhero movies are bad or evil characters. Then, thanks to your experience of encountering Snape throughout the novels, this data would lead you to a position of “knowledge” or even to a position of “wisdom” regarding Snape (and peoples like Snape). This is where what we believe to be “wisdom” can start to cause problems.

—Pause here to note that the backstory of many of the villains in superhero movies are frequently tragic and often not all that different from the backstory of the hero or heroines. Similar data, different narratives, different superhero/ villain costume.—

This is also an example of how data is pre-epistemic. That means data has no truth, it just is, other people come along and clean it up, interpret it, narrativise it, assign it truth-values. Throughout the series Snape just is, he does not explain or excuse himself and he is as true to his love of Lily Potter at the beginning as he is at the end. We just happen not to be privy to his backstory. But Dumbledore is, and Rowling (naturally) is. Without the backstory readers are let run amok regarding the character of Snape, often hating him, feeling anxious in his presence, or fearing for the safety of the characters around him. But how much of that comes from Snape himself, or how much comes from other peoples’ interactions with him and their reading of him? Remember, many of the characters in the Potter-verse have already decided for themselves that Snape is evil and not to be trusted; the narratives we read are fueled by tainted interpretations of his data. Everything from his stare to his tone of voice is presented with adjectives that encourage us to read him as malevolent. The state of “Wisdom” (also, in my case, near-hysterical despair) we arrive at in book six when Snape kills Dumbledore is fuelled by cumulative knowledge and information that stems from misinterpreted data. The Snape-data has been read one way and one way alone, and there is no alternative narrative available, especially not once Dumbledore dies. There is no counter-argument to the data that seems to paint Snape as an absolute villain. None at least until, in his own death scene, he provides the memory (and narrative) that allows Harry to access the “truth value” of Snape’s history and his lifelong love for Lily.

Armed with this poignant insight into Snape’s childhood and history (his personal narrative, his personal account for the data-traces he has left on the Potter-verse), our DIKW pyramid is rewritten from the bottom up, but interestingly, much of it remains the same. The data stays data, but the narratives that we use form information from that data, to create knowledge from that information, and to eventually arrive at a sage-like state of sad wisdom regarding Snape’s sad fate, these narratives change. Snape still says the things he says, and does the things he does, we are just newly wise to his motives. We now know he is a good man. Same data, completely different DIKW.

We already known the DIKW model is problematic and oversimplified. As Christine Borgman notes, the “tripartite division of data, information, and knowledge […] oversimplifies the relationships among these complex constructs.”[1] Data is reinterpretable. And this is key. For the majority of the series Snape is continually heard, seen, and spoken about by characters in the texts using adjectives that assign morally dubious traits to his character. The –IKW part of Snape’s DIKW is unbalanced, because we do not get Snape’s personal narrative until the very end of his story. And it is only through this narrative that we can reassess the data on him we have collected, discarding some of it (such as the tendency to dramatise his appearance into one akin to a stage villain) as absolute rubbish, and reassessing what remains.

While Snape may carry the visual appearance (visual data) that makes it easy for us to suspect or infer that he is a “bad character,” while he may even carry the physiognomical hallmarks that hark back to racist character profiling in English literature, he is essentially a good person. What Rowling is saying here, in the most epic Rowling-esque fashion, is do not judge a person based on their appearance. Judge them on what they do and why they do it. This is Rowling’s real trump card to the “evil is as evil looks” camp of Snape-hating Potter fans. And arguably it is also Rowling’s way of redressing this unpleasant facet of English literary history, which sees race presented through face, and race or racial stereotypes sadly being presented as a measure of a character’s moral compass. Rowling writes back against this tradition by having Snape carry the same facial features of these similarly maligned “villainous” figures, features past readers or audiences would have taken as crude indications of his untrustworthiness. Yet instead of being “untrustworthy,” this same hook-nosed figure turns out to be one of the bravest, strongest, truest characters in the series. Take that Shakespeare and Dickens. Whup-ah.

So, we come back then to the problem of DIKW. Models such as DIKW create misleading and misrepresentative impressions about the supposed distinctions between the various facets of DIKW. DIKW also belies the central role narrative plays in all of this; narrative is the conveyor of information, knowledge, and wisdom. It is how we articulate and spread our opinions on data. And data is the foundation of DIKW, so depending on how that data is narrativised, the other elements in this hierarchy can be drastically different. One sees Snape as evil, and reads this wickedness in and into his every scene, up until his death. The other asks us to think carefully about what we do with our data, and the narratives we create from it, because even when we are wholly convinced in the veracity and justifiableness of our “wisdom,” we could be totally wrong, as we were with Snape.

[1] Christine L. Borgman, “Big Data, Little Data, No Data,” MIT Press, 17, accessed April 7, 2017, https://mitpress.mit.edu/big-data-little-data-no-data, 17.

 

 

The Revolution of the McWord; or, why difference and complexity is necessary.

“It’s a beautiful thing, the destruction of words.”—George Orwell, 1984.

One of my earliest memories of being reprimanded happened when I was in Junior or Senior Infants at Primary School. During a French lesson I needed to use the bathroom (tiny humans often do). We had been told we could only communicate in French, so I sat there attempting to gather and translate my toilet related thoughts into something suitably Francophone. Eventually I put up my hand, got the teacher’s attention, pointed at my chest and said “Moi,” pointed at the door that lead to the bathrooms and said “toilette?” The teacher snapped and said “No Georgina you are not a toilet!”

A little harsh perhaps, especially considering I was a four-year old three-foot high mini-human, but still, I haven’t forgotten it, and now I’m fluent in French. My effort at breaking down a language barrier caused someone to snap and (it seems) be insulted by my tiny human attempt at French.

So certainly I agree with Jennifer’s point from her recent blog article here that “Building intimacy (for this is what I take the phrase “brings you closer” to mean) is not about having a rough idea of what someone is saying, it is about understanding the nuance of every gesture, every reference and resonance.” This is part of the (many) reason(s) why people on the autism spectrum, for example, find social interaction so difficult, because of a difficulty understanding these very gestural nuances that are so central to human communications. And this lack of understanding often brings with it frustration, isolation, loneliness, and pain. The point is: it’s not just about the words; it’s about how they are said, the tone, the gesture, the contexts. These are things a translation program cannot understand or impart, and it is arrogant to suggest that such facets of communication are by-passable or expendable when so many people struggle with them on a day-to-day basis. Moreover, they are facets of human communication that cannot be erased or eliminated from speech-exchanges with a view to making these exchanges “simpler” or “doubleplusgood.” That brings us right into 1984 territory.

From the perspective of Eugene Jolas, author of the “Revolution of the Word” manifesto published in transition magazine, a modernist periodical active in Paris throughout the 1920s and 1930s whose contributors included the likes of James Joyce, Gertrude Stein,  and Samuel Beckett, language was not complex enough:

Tired of the spectacle of short stories, novels, poems and plays still under the hegemony of the banal word, monotonous syntax, static psychology, descriptive naturalism, and desirous of crystallizing a viewpoint… Narrative is not mere anecdote, but the projection of a metamorphosis of reality” and that “The literary creator has the right to disintegrate the primal matter of words imposed on him by textbooks and dictionaries.”[1]

So, language, or rather languages (Jolas was fluent in several and often wrote in an amalgamation that, he felt, better reflected his hybridic Franco-German (Alsatian) and American identity[2]), was not complex enough to fully express the totality of reality.

Mark Zuckerburg proposes something of a devolution, a de-creation, a simplifying of difference, a reintegration and amalgamation of the facets that distinguish us from others. But while it might appear useful (Esperanto, anyone?), is the experience going to lead to richer conversations? To a demolition of barriers? Or will it result in something akin to Point It, the highly successful so-called “Travellers Language Kit” that contains no language at all, but rather an assortment of pictures that allow one to leaf through the book and point at the item you want.

So, with a sensitive interlocutor, one could perhaps intuit that “Me *points at* Coca-Cola” means “Hi, I would like a Coca-Cola.” Or that “Me *points at* toilet” would likely mean “Hi, I desperately need to use your bathroom, could you be so kind as to point me in the right direction?”

After my childhood French toilet incident, I myself could never overcome the mortification involved in using Point It. But even if I did, would the success of an exchange wherein “Me *points at* Coca-Cola” results in my being handed a Coca-Cola give me the same satisfaction as my first successful exchange with someone in another language did? The first time I managed to say something in French to a French person in France and be met with a response in French as opposed to a confused look or (worse) a response in English. Would Zuckerberg’s own much-lauded trip to China in 2014 where he was interviewed and responded in Mandarin have received as much positive press if he had worked through an interpreter, or used a pioneering neural network translation platform? I don’t think so.

Reducing or eliminating language difference also creates hierarchies, and this is dangerous. What language will we agree to communicate in? Why one language and not another? What facets of my individuality are accented in my native language that are perhaps left out or lost in another?

In short, there is an ethical element to this, and one that must be acknowledged and addressed. It’s similar to the argument Todd Presner articulates in “The Ethics of the Algorithm” when he notes the negative affect of reducing human experience (in this case, the testimonies of Holocaust survisors) to keywords so that their experiences become “searchable”: “it abstracts and reduces the human complexity of the victims’ lives to quantized units and structured data. In a word, it appears to be de-humanizing”[3]

We have to resist what Presner calls “the impulse to quantify, modularize, distantiate, technify, and bureaucratize the subjective individuality of human experience,”[4] even if this impulse is driven by a desire to facilitate communications across perceived borders. Finding, maintaining, and celebrating the individual in an era that is putting increasing pressure to separate the “in-” from the “-dividual” for the sake of facility will lead (and has perhaps already lead, if we can refer back to Don DeLillo’s 1985 observation that “You are the sum total of your data.”) to the era of the “dividua”[5]; where instead of championing individuality, people are reduced to their component data sets, or rather the facets of their personhood that can be assigned to data sets, with the rest—the enigmatic “in-” that makes up an individual—deemed unnecessary, a “barrier” to facile communications.

Rather that working to fractalise language, as Eugene Jolas did, universal translation (which is itself a misnomer, all translations are, to a degree, inexact and entail a degree of intuition or creativity to render one word in or through another word) simplifies that which cannot, and should not, be simplified.  This would be doubleplusungood.

Complexity matters.

KPLEX matters.

[1] Eugene Jolas, “Revolution of the Word,” transition 16/17, 1929.

[2] Born in the New Jersey, Jolas moved to Europe the bilingual Alsace-Lorraine region as a young child, and later spent key formative years in the United States.

[3] Presner, in “The Ethics of the Algorithm: Close and Distant Listening to the Shoah Foundation Visual History Archive” in Fogu, Claudio, Kansteiner, Wulf, and Presner, Todd, Probing the Ethics of Holocaust Culture..

[4] Presner, in ibid.

[5] Deleuze, “Postscript on the Societies of Control.”

The rule and question of the eggs: how do we decide what data is the right data?

The rule and question of the eggs.

A young maiden beareth eggs to the market for to sell and her meetheth a young man that would play with her in so much that he overthroweth and breaketh the eggs every one, and will not pay for them. The maid doth him to be called afore the judge. The judge condemneth him to pay for the eggs/ but the judge knoweth not how many eggs there were. And that he demandeth of the maid/ she answereth that she is but young, and cannot well count.[1]

“The rule and question of the eggs” is an arithmetic problem contained within the earliest English-language printed treatise on arithmetic, An Introduction for to Learn to Reckon with the Pen.[2] In addition to being flat-out joyous to read, “The rule and question of the eggs” (along with the other examples Travis D. William’s discusses in his brilliant essay on early modern arithmetic in ‘Raw Data’ is an Oxymoron) raises some important issues that are relevant on a multidisciplinary level in terms of how we approach the archives of our respective fields of study with a view to making them available on digital platforms; furthermore, the “rule and question of the eggs” raises interesting questions in terms of how nuanced cultural artefacts (of which this is but one example) are to be satisfactorily yolked together (see what I did there?) and represented en masse in the form of big data.

It is not easy to define what facets of this piece of hella dodgy arithmetic merit particular attention, or to anticipate what facets of the piece other readers (present day or future readers) may find interesting. If we were to go about entering this into an online archive or database, what keywords would we use? What aspects of this problem are of particular importance? What information is essential, what is inessential? For practitioners of maths, is it the bizarre non-existent non-workable formula that seemingly asks the student to figure out how many eggs the girl was carrying in her basket, despite the volume of the basket being left out and the girl herself professing to not knowing how to count? For historians of mathematics and early modern approaches to the pedagogy of mathematics is it the language used to frame the problem? For sociologists, feminists, or historians (etc.) is it the unsettling reference to a “young man that would play with” a young woman? Is it the fact that the crime being reported is the destruction of eggs as opposed to the public assault? Or is it the fact that the young girl seemingly countered his unwelcome advances by committing fraud because she claimed she had been carrying 721 (!) eggs.[3] Simply put, where or what is the data here? And is the data you pull from this piece the same as the data I pull from it, or the same as the data a reader in fifty years time will pull from it?

Houston, we have a problem: if we go to categorise this example, we risk leaving out any one of the many essential facets that make the “rule and question of the eggs” such a rich historical document. But categorise it we must. And, just like moving between languages (English-to-French or vice versa), when we move from rich, nuanced, ambiguous and complex linguistic narratives to the language of the database, the dataset, the encoded set of assigned unambiguous values readable to computers, we expose the material to a translation act that imposes delimiting interpretations on that material by creating datasets that drastically simplify the material. Someone decides what is worth translating, what is incidental, and what should be left out here. Someone converts the information as it stands in one language (linguistic narrative), into supposedly comparable or equivalent information in another language (computer narrative).

Further still, by translating this early modern piece into information that is workable within the sphere of contemporaneous digital archival practices, we create a scenario wherein the material is no longer read as it was intended to be read. We impose our thinking and understanding about maths (or women’s rights, say, because the “play with” part really irks me) onto their text by taking it and making of it a series of functional datasets relevant to our particular scholarly interests. As Williams points out “a data set is already interpreted by the fact that is a set: some elements are privileged by inclusion; while others are denied relevance through exclusion.”[4]

In analysing these pieces Williams establishes four terminologies: “our reading and their reading, our rigor and their rigor.”[5] He elaborates:

Our reading is a practice of interpretation that seeks to understand the appearance and function of texts within their original historical and cultural milieus. Our reading thus incorporates the need to understand with nuance their reading: why and how contemporaneous readers would read the texts produced by their cultures.[6]

That’s all well and good when approached from an analogue-dependent research environment where one is tackling these early modern maths problems one by one. After all, this is merely one maths problem within an entire book containing maths problems. But what if we were to take it to a Borgesian level, to a big data level wherein this “rule of the eggs” is merely one math problem within an entire book containing maths problems, a book contained within a library containing books that contain only maths problems; a library that was in fact the ur-library of maths books, containing every maths book and every maths problem ever written.

When we amp up the scale to the realm of big data and this one tiny problem becomes one tiny problem within an entire ur-library of information, how do we stay cognisant of the fact that every entry in a given dataset, no matter how seemingly incidental or minute, could be as detailed and nuanced as our enigmatic rule and question of the eggs?

[1] Quoted in Travis D. Williams “Procrustean Marxism and Subjective Rigor,” Gitelman, “Raw Data” is an Oxymoron, 45.

[2] In am indebted to Travis D. Williams’s essay “Procrustean Marxism and Subjective Rigor: Early Modern Arithmetic and Its Readers” (to be found in “Raw Data” is an Oxymoron (2013)) for bringing these incredible examples to light.

[3] Williams notes that the “correct” answer (or rather the answer recorded in the arithmetic book as the “correct” answer) is 721 eggs. But this would mean that the young maiden carrying roughly 36 kilos (yes, I’ve done the math) of egg, which seems unlikely. Williams “Procrustean Marxism and Subjective Rigor,” ibid.

[4] Travis D. Williams “Procrustean Marxism and Subjective Rigor,” 41.

[5] Travis D. Williams “Procrustean Marxism and Subjective Rigor,” Gitelman, “Raw Data” is an Oxymoron, 42.

[6] Travis D. Williams “Procrustean Marxism and Subjective Rigor,” ibid.

Featured image was taken from http://www.flickr.com

Tinfoil hats, dataveillance, and panopticons.

When I started my work with KPLEX, I was not expecting to encounter so many references to literature. Specifically, to works of fiction I have read in my capacity as an erstwhile undergraduate and graduate student of literature who had (and still has) a devout personal interest in the very particular, paranoid postmodern fictions that crawled out of the Americas (North and South) like twitchy angst-ridden spiders in the mid-to-latter half of the 20th century. The George Orwell references did not surprise me all that much; after all, everyone loves to reference 1984. But Jorge Luis Borges, Thomas Pynchon, and Don DeLillo? These guys produced (the latter two are still producing) the kind of paranoiac post-Orwellian literature that could be nicely summed up by the Nirvana line “Just because you’re paranoid/ Don’t mean they’re not after you,” which is itself a slightly modified lift straight out of Joseph Heller’s Catch 22.Pynchon-simpsons.0.0      

It seems, however, that when it comes to outlining, theorising and speculating over the state, uses, and value of data in 21st century society, the paranoid tinfoil hat wearing Americans and their close predecessor, the Argentinian master of the labyrinth, got there first.

We are all by now familiar with—or have at least likely heard reference to—the surveillance system in operation in 1984; a two-way screen that captures image and sound so that the inhabitants of Orwell’s world are always potentially being watched and listened to. In a post-Snowden era this all-seeing all-hearing panoptic Orwellian entity has already been referenced to death, and indeed, as Rita Raley points out, Orwell’s two-way screen has long been considered inferior to the “disciplinary and control practice of monitoring, aggregating, and sorting data.”[1] In other words, to the practice of “dataveillance.[2] But Don DeLillo’s vision of the role data would play in our future was somewhat different, more nuanced, and most importantly, is less overtly classifiable as dystopian; in fact, it reads rather like a description of an assiduous Google Search, yet it is to be found in the pages of a book first published in 1985:

It’s what we call a massive data-base tally. Gladney, J.A.K. I punch in the name, the substance, the exposure time and then I tap into your computer history. Your genetics, your personals, your medicals, your psychologicals, your police-and-hospitals. It comes back pulsing stars. This doesn’t mean anything is going to happen to you as such; at least not today or tomorrow. It just means you are the sum total of your data. No man escapes that.[3]

Dataveillance is interesting because its function is not just to record and monitor, but also to speculate, to predict, and maybe even to prescribe. As a result, as Raley points out, its future value is speculative: “it awaits the query that would produce its value.”[4] By value Raley is referring to the economic value this data may have in terms of its potential to influence people to buy and sell things; and so, we have a scenario wherein data is traded in a manner akin to shares or currency, where “data is the new oil of the internet”:[5]

Data speculation means amassing data so as to produce patterns, as opposed to having an idea for which one needs to collect supporting data. Raw data is the material for informational patterns to come, its value unknown or uncertain until it is converted into the currency of information. And a robust data exchange, with so-termed data handlers and data brokers, has emerged to perform precisely this work of speculation. An illustrative example is BlueKai, “a marketplace where buyers and sellers trade high-quality targeting data like stocks,” more specifically, an auction for the near-instant circulation of user intent data (keyword searches, price searching and product comparison, destination cities from travel sites, activity on loan calculators).[6]

This environment of highly sophisticated, near-constant amassing of data leads us back to DeLillo and his observation, made back in 1985, that “you are the sum total of your data.” And this is perhaps the very environment that leads Geoffrey Bowker to declare, in his provocative afterword to the collection of essays ‘Raw Data’ is an Oxymoron (2013), that we as humans are “entering into”, are “being entered” into, “the dataverse.”[7] Within this dataverse, Bowker—who is being self-consciously hyperbolic—claims it is possible to “take the unnecessary human out of the equation,” envisioning a scenario wherein “our interaction with the world and each other is being rendered epiphenomenal to these data-program-data cycles” and one where, ultimately, “if you are not data, you don’t exist.”[8] But this is precisely where we must be most cautious, particularly when it comes to the nascent dataverse of humanities researchers. Because while we might tentatively make the claim to be within a societal dataverse now, the alignment of data with existence and experience is still far from total. We cannot yet fully capture the entirety of the white noise of selfhood.

And this is where things start to get interesting, because what is perhaps dystopian from a contemporaneous perspective—that is, the presence somewhere out there of near infinitesimal quanta of data pertaining to you, your preferences, your activities— a scenario that might reasonably lead us to reach for those tinfoil hats, is, conversely, a desirable one from the perspective of historians and other humanities researchers. A data sublime, a “single database fantasy”[9] wherein one could access everything, where nothing is hidden, and where the value, the intellectual, historical, and cultural value of the raw data is always speculative, always potentially of value to the researcher, and thus amassed and maintained with the same reverence associated with high value data traded today on platforms such as BlueKai. Because as it is, the amassing of big data for humanities researchers, particularly when it comes to converting extant analogue archives and collections, subjects the material to a hierarchising process wherein items of potential future value (speculative value) are left out or hidden; greatly diminishing their accessibility and altering the richness or fertility of the research landscape available to future scholars. After all, “if you are not data, you don’t exist.”[10] But if you don’t exist then, to paraphrase Raley, you cannot be subjected to the search or query of future scholars and researchers, the search or query that would determine your value.

As we move towards these data sublime scenarios, it is important not to lose sight of the fact that that which is considered data now, this steadily accumulating catalogue of material pertaining to us as individuals or humans en masse, still does not capture everything. And if this is true now then it is doubly true (ability to resist Orwellian doublespeak at this stage in blogpost = zero) of our past selves and the analogue records that constitute the body of humanities research. How do we incorporate the “not data” in an environment where data is currency?

Happy Day of DH!

[1] Raley, “Dataveillance and Countervailance” in Gitelman ed., “Raw Data” is an Oxymoron, 124.

[2] Roger Clarke, quoted in ibid.

[3] Don DeLillo, White Noise, quoted in Gitelman ed., “Raw Data” is an Oxymoron, 121, emphasis in original.

[4] Raley, “Dataveillance and Countervailance” in ibid., 123–4.

[5] Julia Angwin, “The Web’s New Gold Mine: Your Secrets,” quoted in ibid., 123.

[6] Raley, “Dataveillance and Countervailance” in ibid., 123.

[7] Geoffrey Bowker, “Data Flakes: An Afterword to ‘Raw Data’ Is an Oxymoron” in ibid., 167.

[8] Bowker, in ibid., 170.

[9] Raley, “Dataveillance and Countervailance” in ibid., 128

[10] Bowker, in ibid., 170.

Featured image is a still taken the film version of 1984.

Big Data, Little Data, Fabulous Data.

Suzanne Briet, in What is documentation? says that “Documentography is the enumeration and description of diverse documents.”[1] Slightly modified, and paired with a nice little neologism (who can resist neologisms?) I could describe the work I am doing at this stage in the KPLEX Project as “Datamentography.Datamentography, of course, meaning the enumeration and description of diverse data. I’m working to establish what it is we talk about when we talk about data; the established conceptions of what data is among different communities, the why, how and where that lead to the development of the various understandings and conceptions of data active today. Once this has been established, we can use these findings to move towards a new conceptualisation of data.

One of the other passages in Briet that I really like (because it is quite poetic and this is perhaps a little unexpected in a text about documentation standards) is as follows:

Is a star a document? Is a pebble rolled by a torrent a document? Is a living animal a document? No. But the photographs and the catalogues of stars, the stones in a museum of mineralogy, and the animals that are catalogued and shown in a zoo, are documents.[2]

So, this nice descriptive passage outlines a key distinction: the thing itself is not a document, but the material traces of its interactions with humans are; the photos, the specimens, the catalogues, the records (visual, audio, and so on). But how do we capture the richness of these items in a computerised environment? A pebble is one thing, as is a stone in a museum of mineralogy. How to capture the pebble rolling in a torrent? And how to do so in a manner that does not subject the material to an interpretative act that alters how future scholars and researchers approach these records? If all the future scholar can “see” in the online repository is what the person responsible for compiling the repository considered to be important (or codeable) then their interpretative sphere is corrupted (if that is not too dramatic a word) from the onset.

Choice emerges as an implicit facet in this distinction; irrespective of how objective we think we are being, the act of collating information is implicitly subjective. What one person identifies as important (as worthy of documenting, as data), may appear wholly unimportant to someone else, and vice versa. Taste and preference are fine in day-to-day life (“You say tomato, I say tomahto… You say potato, I say vodka…”), but when these inherently human and therefore unavoidable subjective tendencies are let loose on humanities repositories, then a hierarchy is imposed on knowledge that reflects the subjective choices of the person who has classified or codefied them.

Further still, encoding the thing-ness of things is difficult. In a society that increasingly values and priorities codefied data, if what is readily codeable is prioritisied without concordant measures taken to account for the facets of human records and experiences that do not lend themselves so readily to codification, we encounter a scenario wherein that which is not as readily codeable is left out, neglected or even forgotten.

Now, people have gone about defining data in a number of different ways, and almost all are at least a little problematic. Christine Borgman, in her book chapter “What are Data?” from Big Data, Little Data, No Data uses an example from the great Argentinian writer Jorge Luis Borges to explain why defining data by example is unsatisfactory. In his essay “The Analytical Language of John Wilkins” Borges presents us with a taxonomy of animals in the form of a Chinese encyclopedia, Celestial Emporium of Benevolent Knowledge. In this taxonomy we encounter the following classifications:

a) belonging to the emperor, b) embalmed, c) tame, d) sucking pigs, e) sirens, f) fabulous, g) stray dogs, h) included in the present classification, i) frenzied, j) innumerable k) drawn with a very fine camelhair brush, l) et cetera m) having just broken the water pitcher, n) that from a long way off look like flies.

Of course, this list is somewhat absurd, and its absurdity is what makes its funny and what makes Borges so brilliant; but this absurdity should not bely the critique of taxonomic practices that lies at the heart of this so-called “emporium of benevolent knowledge.” Lets take a closer look.

Embalmed animals are included because someone once identified them as worthy of embalming, and that the act of being embalmed somehow signified something that was worth documenting (in the form of putting the sad creature in a jar of formaldehyde; or rather, of making a record of the fact that this creature has been stored in formaldehyde). Similarly, some animals are included merely because they are already in the system (“included in the present classification”), so simply because they are already there and it is easy to carry them over and keep them incorporated; in this way long established practices are maintained, simply because they are long established and not necessarily because they are effective (hello metadata, you cheeky old fox).

In What is documentation? Briet charts the sad odyssey of an “antelope of a new kind […] encountered in Africa by an explorer who has succeeded in capturing an individual that is then brought back to Europe for our Botanical Garden”[3]:

A press release makes the event known by newspaper, by radio, and by newsreels. The discovery becomes the topic of an announcement at the Academy of Sciences. A professor at the Museum discusses it in his courses. The living animal is placed in a cage and catalogued (zoological garden). Once it is dead, it will be stuffed and preserved (in the Museum). It is loaned to an Exposition. It is played on a soundtrack at the cinema. Its voice is recorded on a disk. The first monograph serves to establish part of a treatise with plates, then a special encyclopaedia (zoological), then a general encyclopaedia. The works are catalogued in a library; after having been announced at publication [et cetera].[4]

So we have the genesis here of the thing itself—a “pure or natural object with an essence of [its] own”[5]—from its capture and discovery through to its death when it is stuffed (akin to Borges’s embalming) and a process is initiated wherein it is catalogued and subjected to extensive “documentography” according to the established taxonomy of “Homo documentator.”[6] “Homo documentator” creates detailed portraits of the creature (perhaps using a very fine camelhair brush as in Borges’s encyclopaedia) for inclusion in the classificatory system, records its unique markings, the sound of its voice, whatever aspects of the creatures essence can be readily captured. Once in the system, whether by means of artistic plates outlining specifics of the species, or in the form of photographs and sound recording, and so on, it becomes a de-facto document (de-facto data), and its documentability is exhausted only when the taxonomical system employed by the documentalist has itself been exhausted.

But who has designed this taxonomical system? Who is responsible for deciding what facets of the antelope are important and what not? Are items perhaps ever considered important solely because they are facile to document? And who is to say that this same sad and now stuffed antelope could not also be classified as fabulous (or, once fabulous, had you encountered it in the wild)? Further still, surely this creature, like all creatures when viewed from a certain perspective, could be included in Borges’s category for animals that “from a long way off look like flies.” The point is that the system of taxonomy is not objective, and our conceptions of the facets that are important or unimportant can and have been influenced by the hierarchies imposed upon them by the person or persons responsible for compiling them.

Borgman in “What Are Data?” refers us to Open Archival Information System (OAIS) for a definition of data that, once again, uses examples:

Data: A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing. Examples of data include a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a person speaking, or a moon rock specimen.[7]

This, like most definitions of data, seems relatively reasonable at first, naturally the characters on a page are going to qualify as data, and so if they do, they are or can be encoded as such. But what about the page itself? What about the materials on the page that do not qualify as characters? What about doodles? Pen-tests? Scribbles, drawings, additions and other contextually specific parlipomena? How do we encode these? And, if we decide not to, why do we decide not to, and who—if anyone—holds us accountable for that decision? Because such a decision, inconsequential though it may seem at first, could effect and limit future scholars.

And this is what I am attempting to tease out as part of my contribution to the KPLEX Project: why and how did certain conceptions of data become acceptable or dominant in certain circles, and, going forward, as we move towards bigger data infrastructures for the humanities, is there a way for us to ensure that the thing itself, in all its complex idiosyncratic fabulousness, remains visible, and available to the researcher?

[1] Briet et al., What Is Documentation?, 24.

[2] Ibid., 10.

[3] Ibid., 10.

[4] Ibid.

[5] Borgman, “Big Data, Little Data, No Data,” 18.

[6] Briet et al., What Is Documentation?, 29.

[7] Borgman, “Big Data, Little Data, No Data,” 20, emphasis in original.

The Featured Image was borrowed from Natascha Schwarz’s illustrated edition of Borges’s Book of Imaginary Beings: https://www.behance.net/gallery/10823485/Jorge-Luis-Borges-Book-of-Imaginary-Beings