After having spent the last five weeks blogging about about algorithmic culture, I figured both you and I deserved a change of pace.  I’d like to share some new research of mine that was just published in a free, Open Access periodical called The International Journal of Communication. 

My piece is called “The Visible College.”  It addresses the many ways in which the form of scholarly publications — especially that of journal articles — obscures the density of the collaboration typical of academic authorship in the humanities.  Here’s the first line: “Authorship may have died at the hands of a French philosopher drunk on Balzac, but it returned a few months later, by accident, when an American social psychologist turned people’s attention skyward.”  Intrigued?

My essay appears as part of a featured section on the politics of academic labor in the discipline of communication.  The forum is edited by my good friend and colleague, Jonathan Sterne.  His introductory essay is a must-read for anyone in the field — and, for that matter, anyone who receives a paycheck for performing academic labor.  (Well, maybe not my colleagues in the Business School….)  Indeed it’s a wonderful, programmatic piece outlining how people in universities can make substantive change there, both individually and collectively.  The section includes contributions from: Thomas A. Discenna; Toby Miller; Michael Griffin; Victor Pickard; Carol Stabile; Fernando P. Delgado; Amy Pason; Kathleen F. McConnell; Sarah Banet-Weiser and Alexandra Juhasz; Ira Wagman and Michael Z. Newman; Mark Hayward; Jayson Harsin; Kembrew McLeod; Joel Saxe; Michelle Rodino-Colocino; and two anonymous authors.  Most of the essays are on the short side, so you can enjoy the forum in tasty, snack-sized chunks.

My own piece presented me with a paradox.  Here I was, writing about how academic journal articles do a lousy job of representing all the labor that goes into them — in the form of an academic journal article!  (At least it’s a Creative Commons-licensed, Open Access one.)  Needless to say, I couldn’t leave it at that.  I decided to create a dossier of materials relating to the production of the essay, which I’ve archived on another of my websites, The Differences and Repetitions Wiki (D&R^W).  The dossier includes all of my email exchanges with Jonathan Sterne, along with several early drafts of the piece.  It’s astonishing to see just how much “The Visible College” changed as a result of my dialogue with Jonathan.  It’s also astonishing to see, then, just how much of the story of academic production gets left out of that slim sliver of “thank-yous” we call the acknowledgments.

“The Visible College Dossier” is still a fairly crude instrument, admittedly.  It’s an experiment — one among several others hosted on D&R^W in which I try to tinker with the form and content of scholarly writing.  I’d welcome your feedback on this or any other of my experiments, not to mention “The Visible College.”

Enjoy — and happy Halloween!  Speaking of which, if you’re looking for something book related and Halloween-y, check out my blog post from a few years ago on the topic of anthropodermic bibliopegy.

Lest there be any confusion, yes, indeed, you’re reading The Late Age of Print blog, still authored by me, Ted Striphas.  The last time you visited, the site was probably red, white, black, and gray.  Now it’s not.  I imagine you’re wondering what prompted the change.

The short answer is: a hack.  The longer answer is: algorithmic culture.

At some point in the recent past, and unbeknownst to me, The Late Age of Print got hacked.  Since then I’ve been receiving sporadic reports from readers telling me that their safe browsing software was alerting them to a potential issue with the site.  Responsible digital citizen that I am, I ran numerous malware scans using multiple scanning services.  Only one out of twenty-three of those services ever returned a “suspicious” result, and so I figured, with those odds, that the one positive must be an anomaly.  It was the same service that the readers who’d contacted me also happened to be using.

Well, last week, Facebook implemented a new partnership with an internet security company called Websense.  The latter checks links shared on the social networking site for malware and the like.  A friend alerted me that an update I’d posted linking to Late Age came up as “abusive.”  That was enough; I knew something must be wrong.  I contacted my web hosting service and asked them to scan my site.  Sure enough, they found some malicious code hiding in the back-end.

Here’s the good news: as far as my host and I can tell, the code — which, rest assured, I’ve cleaned — had no effect on readers of Late Age or your computers.  (Having said that, it never hurts to run an anti-virus/malware scan.)  It was intended only for Google and other search engines, and its effects were visible only to them.  The screen capture, below, shows how Google was “seeing” Late Age before the cleanup.  Neither you nor I ever saw anything out of the ordinary around here.

Essentially the code grafted invisible links to specious online pharmacies onto the legitimate links appearing in many of my posts.  The point of the attack, when implemented widely enough, is to game the system of search.  The victim sites all look as if they’re pointing to whatever website the hacker is trying to promote. And with thousands of incoming links, that site is almost guaranteed to come out as a top result whenever someone runs a search query for popular pharma terms.

So, in case you were wondering, I haven’t given up writing and teaching for a career hocking drugs to combat male-pattern baldness and E.D.

This experience has been something of an object lesson for me in the seedier side of algorithmic culture.  I’ve been critical of Google, Amazon, Facebook, and other such sites for the opacity of the systems by which they determine the relevance of products, services, knowledge, and associations.  Those criticisms remain, but now I’m beginning to see another layer of the problem.  The hack has shown me just how vulnerable those systems are to manipulation, and how, then, the frameworks of trust, reputation, and relevance that exist online are deeply — maybe even fundamentally — flawed.

In a more philosophical vein, the algorithms about which I’ve blogged over the last several weeks and months attempt to model “the real.”  They leverage crowd wisdom — information coming in the form of feedback — in an attempt to determine what the world thinks or how it feels about x.  The problem is, the digital real doesn’t exist “out there” waiting to be discovered; it is a work in progress, and much like The Matrix, there are those who understand far better than most how to twist, bend, and mold it to suit their own ends.  They’re out in front of the digital real, as it were, and their actions demonstrate how the results we see on Google, Amazon, Facebook, and elsewhere suffer from what Meaghan Morris has called, in another context, “reality lag.”  They’re not the real; they’re an afterimage.

The other, related issue here concerns the fact that, increasingly, we’re placing the job of determining the digital real in the hands of a small group of authorities.  The irony is that the internet has long been understood to be a decentralized network and lauded, then, for its capacity to endure even when parts of it get compromised.  What the hack of my site has underscored for me, however, is the extent to which the internet has become territorialized of late and thus subject to many of the same types of vulnerabilities it was once thought to have thwarted.  Algorithmic culture is the new mass culture.

Moving on, I’d rather not have spent a good chunk of my week cleaning up after another person’s mischief, but at least the attack gave me an excuse to do something I’d wanted to do for a while now: give Late Age a makeover.  For awhile I’ve been feeling as if the site looked dated, and so I’m happy to give it a fresher look.  I’m not yet used to it, admittedly, but of course feeling comfortable in new style of anything takes time.

The other major change I made was to optimize Late Age for viewing on mobile devices.  Now, if you’re visiting using your smart phone or tablet computer, you’ll see the same content but in significantly streamlined form.  I’m not one to believe that the PC is dead — at least, not yet — but for better or for worse it’s clear that mobile is very much at the center of the internet’s future.  In any case, if you’re using a mobile device and want to see the normal Late Age site, there’s a link at the bottom of the screen allowing you to switch back.

I’d be delighted to hear your feedback about the new Late Age of Print.  Drop me a line, and thanks to all of you who wrote in to let me know something was up with the old site.

In my previous post I addressed the question, who speaks for culture in an algorithmic age?  My claim was that humanities scholars once held significant sway over what ended up on our cultural radar screens but that, today, their authority is diminishing in importance.  The work of sorting, classifying, hierarchizing, and curating culture now falls increasingly on the shoulders of engineers, whose determinations of what counts as relevant or worthy result from computational processes.  This is what I’ve been calling, “algorithmic culture.”

The question I want to address this week is, what assumptions about culture underlie the latter approach?  How, in other words, do engineers — particularly computer scientists — seem to understand and then operationalize the culture part of algorithmic culture?

My starting point is, as is often the case, the work of cultural studies scholar Raymond Williams.  He famously observed in Keywords (1976) that culture is “one of the two or three most complicated words in the English language.”  The term is definitionally capacious, that is to say, a result of centuries of shedding and accreting meanings, as well as the broader rise and fall of its etymological fortunes.  Yet, Williams didn’t mean for this statement to be taken as merely descriptive; there was an ethic implied in it, too.  Tread lightly in approaching culture.  Make good sense of it, but do well not to diminish its complexity.

Those who take an algorithmic approach to culture proceed under the assumption that culture is “expressive.”  More specifically, all the stuff we make, practices we engage in, and experiences we have cast astonishing amounts of information out into the world.  This is what I mean by “cultural informatics,” the title of this post.  Algorithmic culture operates first of all my subsuming culture under the rubric of information — by understanding culture as fundamentally, even intrinsically, informational and then operating on it accordingly.

One of the virtues of the category “information” is its ability to link any number of seemingly disparate phenomena together: the movements of an airplane, the functioning of a genome, the activities of an economy, the strategies in a card game, the changes in the weather, etc.  It is an extraordinarily powerful abstraction, one whose import I have come to appreciate, deeply, over the course of my research.

The issue I have pertains to the epistemological entailments that flow from locating culture within the framework of information.  What do you have to do with — or maybe to — culture once you commit to understanding it informationally?

The answer to this question begins with the “other” of information: entropy, or the measure of a system’s disorder.  The point of cultural informatics is, by and large, to drive out entropy — to bring order to the cultural chaos by ferreting out the signal that exists amid all the noise.  This is basically how Google works when you execute a search.  It’s also how sites like Amazon.com and Netflix recommend products to you.  The presumption here is that there’s a logic or pattern hidden within culture and that, through the application of the right mathematics, you’ll eventually come to find it.

There’s nothing fundamentally wrong with this understanding of culture.  Something like it has kept anthropologists, sociologists, literary critics, and host of others in business for well over a century.  Indeed there are cultural routines you can point to, whether or not you use computers to find them.  But having said that, it’s worth mentioning that culture consists of more than just logic and pattern.  Intrinsic to culture is, in fact, noise, or the very stuff that gets filtered out of algorithmic culture.

At least, that’s what more recent developments within the discipline of anthropology teach us.  I’m thinking of Renato Rosaldo‘s fantastic book Culture and Truth (1989), and in particular of the chapter, “Putting Culture in Motion.”  There Rosaldo argues for a more elastic understanding of culture, one that refuses to see inconsistency or disorder as something needing to be purged.  “We often improvise, learn by doing, and make things up as we go along,” he states.  He puts it even more bluntly later on: “Do our options really come down to the vexed choice between supporting cultural order or yielding to the chaos of brute idiocy?”

The informatics of culture is oddly paradoxical in that it hinges on a more and less powerful conceptualization of culture.  It is more powerful because of the way culture can be rendered equivalent, informationally speaking, with all of those phenomena (and many more) I mentioned above.  And yet, it is less powerful because of the way the livingness, the inventiveness — what Eli Pariser describes as the “serendipity” — of culture must be shed in the process of creating that equivalence.

What is culture without noise?  What is culture besides noise?  It is a domain of practice and experience diminished in its complexity.  And it is exactly the type of culture Raymond Williams warned us about, for it is one we presume to know but barely know the half of.

I’ve blogged off and on over the past 15 months about “algorithmic culture.”  The subject first came to my attention when I learned about the Amazon Kindle’s “popular highlights” feature, which aggregates data about the passages Kindle owners have deemed important enough to underline.

Since then I’ve been doing a fair amount of algorithmic culture spotting, mostly in the form of news articles.  I’ve tweeted about a few of them.  In one case, I learned that in some institutions college roommate selection is now being determined algorithmically — often, by  matching up individuals with similar backgrounds and interests.  In another, I discovered a pilot program that recommends college courses based on a student’s “planned major, past academic performance, and data on how similar students fared in that class.”  Even scholarly trends are now beginning to be mapped algorithmically in an attempt to identify new academic disciplines and hot-spots.

There’s much to be impressed by in these systems, both functionally and technologically.  Yet, as Eli Pariser notes in his highly engaging book The Filter Bubble, a major downside is their tendency to push people in the direction of the already known, reducing the possibility for serendipitous encounters and experiences.

When I began writing about “algorithmic culture,” I used the term mainly to describe how the sorting, classifying, hierarchizing, and curating of people, places, objects, and ideas was beginning to be given over to machine-based information processing systems.  The work of culture, I argued, was becoming increasingly algorithmic, at least in some domains of life.

As I continue my research on the topic, I see an even broader definition of algorithmic culture starting to emerge.  The preceding examples (and many others I’m happy to share) suggest that some of our most basic habits of thought, conduct, and expression — the substance of what Raymond Williams once called “culture as a whole way of life” — are coming to be affected by algorithms, too.  It’s not only that cultural work is becoming algorithmic; cultural life is as well.

The growing prevalence of algorithmic culture raises all sorts of questions.  What is the determining power of technology?  What understandings of people and culture — what “affordances” — do these systems embody? What are the implications of the tendency, at least at present, to encourage people to inhabit experiential and epistemological enclaves?

But there’s an even more fundamental issue at stake here, too: who speaks for culture?

For the last 150 years or so, the answer was fairly clear.  The humanities spoke for culture and did so almost exclusively.  Culture was both its subject and object.  For all practical purposes the humanities “owned” culture, if for no other reason than the arts, language, and literature were deemed too touchy-feely to fall within the bailiwick of scientific reason.

Today the tide seems to be shifting.  As Siva Vaidhyanathan has pointed out in The Googlization of Everything, engineers — mostly computer scientists — today hold extraordinary sway over what does or doesn’t end up on our cultural radar.  To put it differently, amid the din of our pubic conversations about culture, their voices are the ones that increasingly get heard or are perceived as authoritative.  But even this statement isn’t entirely accurate, for we almost never hear directly from these individuals.  Their voices manifest themselves in fragments of code and interface so subtle and diffuse that the computer seems to speak, and to do so without bias or predilection.

So who needs the humanities — even the so-called “digital humanities” — when your Kindle can tell you what in your reading you ought to be paying attention to?

About a week ago Michael Eisen, who teaches evolutionary biology at UC Berkeley, blogged about a shocking discovery one of his postdocs had made in early April.  The discovery happened not in his lab, but of all places on Amazon.com.

While searching the site for a copy of Peter Lawrence’s book The Making of a Fly (1992), long out of print, the postdoc happened across two merchants selling secondhand editions for — get this — $1.7 million and $2.2 million respectively!  A series of price escalations ensued as Eisen returned to the product page over following days and weeks until one seller’s copy topped out at $23 million.

But that’s not the worst of it.  One of the comments Eisen received on his blog post pointed to a different secondhand book selling on Amazon for $900 million.  It wasn’t an original edition of the Gutenberg Bible from 1463, nor was it a one-of-a-kind art book, either.  What screed was worth almost $1 billion?  Why, a paperback copy of actress Lana Turner’s autobiography, published in 1991, of course!  (I suspect the price may change, so in the event that it does, here’s a screen shot showing the price on Saturday, April 30th.)

Good scientist that he is, Eisen hypothesized that something wasn’t right about the prices on the fly book.  After all, they seemed to be adjusting themselves upward each time he returned to the site, and like two countries engaged in an arms race, they always seemed to do so in relationship to each other.  Eisen crunched some numbers:

On the day we discovered the million dollar prices, the copy offered by bordeebook [one of the sellers] was 1.270589 times the price of the copy offered by profnath [the other seller].  And now the bordeebook copy was 1.270589 times profnath again. So clearly at least one of the sellers was setting their price algorithmically in response to changes in the other’s price. I continued to watch carefully and the full pattern emerged. (emphasis added)

So the culprit behind the extraordinarily high prices wasn’t a couple of greedy (or totally out of touch) booksellers.  It was, instead, the automated systems — the computer algorithms — working behind the scenes in response to perceived market dynamics.

I’ve spent the last couple of blog posts talking about algorithmic culture, and I believe what we’re seeing here — algorithmic pricing — may well be an extension of it.

It’s a bizarre development.  It’s bizarre not because computers are involved in setting prices (though in this case they could have been doing a better job of it, clearly).  It is bizarre because of the way in which algorithms are being used to disrupt and ultimately manipulate — albeit not always successfully — the informatics of markets.

Indeed, I’m becoming  convinced that algorithms (at least as I’ve been talking about them) are a response to the decentralized forms of social interaction that grew up out of, and against, the centralized forms of culture, politics, and economics that were prevalent in the second and third quarters of 20th century.  Interestingly, the thinkers who conjured up the idea of decentralized societies often turned to markets — and more specifically, to the price system — in an attempt to understand how individuals distributed far and wide could effectively coordinate their affairs absent governmental and other types of intervention.

That makes me wonder: are the algorithms being used on Amazon and elsewhere an emergent form of “government,” broadly understood?  And if so, what does a billion dollar book say about the prospects for good government in an algorithmic age?

I learned last month from Wired that something along the lines of what I’ve been calling “algorithmic culture” already has a name — culturomics.

According to Jonathan Keats, author of the magazine’s monthly “Jargon Watch” section, culturomics refers to “the study of memes and cultural trends using high-throughput quantitative analysis of books.”  The term was first noted in another Wired article, published last December, which reported on a study using Google books to track historical, or “evolutionary,” trends in language.  Interestingly, the study wasn’t published in a humanities journal.  It appeared in Science.

The researchers behind culturomics have also launched a website allowing you to search the Google book database for keywords and phrases, to “see how [their] usage frequency has been changing throughout the past few centuries.”  They follow up by calling the service “addictive.”

Culturomics weds “culture” to the suffix “-nomos,” the anchor for words like economics, genomics, astronomy, physiognomy, and so forth.  “-Nomos” can refer either to “the distribution of things” or, more specifically, to a “worldview.”  In this sense culturomics refers to the distribution of language resources (words) in the extant published literature of some period and the types of frameworks for understanding those resources embody.

I must confess to being intrigued by culturomics, however much I find the term to be clunky. My initial work on algorithmic culture tracks language changes in and around three keywords — information, crowd, and algorithm, in the spirit of Raymond Williams’ Culture and Society – and has given me a new appreciation for both the sociality of language and its capacity for transformation.  Methodologically culturomics seems, well, right, and I’ll be intrigued to see what a search for my keywords on the website might yield.

Having said that, I still want to hold onto the idea of algorithmic culture.  I prefer the term because it places the algorithm center-stage rather than allowing it to recede into the background, as does culturomics.  Algorithmic culture encourages us to see computational process not as a window onto the world but as an instrument of order and authoritative decision making.  The point of algorithmic culture, both terminologically and methodologically, is to help us understand the politics of algorithms and thus to approach them and the work they do more circumspectly, even critically.

I should mention, by the way, that this is increasingly how I’ve come to understand the so-called “digital humanities.”  The digital humanities aren’t just about doing traditional humanities work on digital objects, nor are they only about making the shift in humanities publishing from analog to digital platforms.  Instead the digital humanities, if there is such a thing, should focus on the ways in which the work of culture is increasingly delegated to computational process and, more importantly, the political consequences that follow from our doing so.

And this is the major difference, I suppose, between an interest in the distribution of language resources — culturomics – and a concern for the politics of the systems we use to understand those distributions — algorithmic culture.

Back in June I blogged here about “Algorithmic Culture,” or the sorting, classifying, and hierarchizing of people, places, objects, and ideas using computational processes.  (Think Google search, Amazon’s product recommendations, who gets featured in your Facebook news feed, etc.)  Well, for the past several months I’ve been developing an essay on the theme, and it’s finally done.  I’ll be debuting it at Vanderbilt University’s “American Cultures in the Digital Age” conference on Friday, March 18th, which I’m keynoting along with Kelly Joyce (College of William & Mary), Cara Finnegan (University of Illinois), and Eszter Hargittai (Northwestern University).  Needless to say, I’m thrilled to be joining such distinguished company at what promises to be, well, an event.

The piece I posted originally on algorithmic culture generated a surprising — and exciting — amount of response.  In fact, nine months later, it’s still receiving pingbacks, I’m pretty sure as a result of its having found its way onto one or more college syllabuses.  So between that and the good results I’m seeing in the essay, I’m seriously considering developing the material on algorithmic culture into my next book.  Originally after Late Age I’d planned on focusing on contemporary religious publishing, but increasingly I feel as if that will have to wait.

Drop by the conference if you’re in or around the Nashville area on Friday, March 18th.  I’m kicking things off starting at 9:30 a.m.  And for those of you who can’t make it there, here’s the title slide from the PowerPoint presentation, along with a little taste of the talk’s conclusion:

This latter definition—culture as authoritative principle—is, I believe, the definition that’s chiefly operative in and around algorithmic culture. Today, however, it isn’t culture per se that is a “principle of authority” but increasingly the algorithms to which are delegated the task of driving out entropy, or in Matthew Arnold’s language, “anarchy.”  You might even say that culture is fast becoming—in domains ranging from retail to rental, search to social networking, and well beyond—the positive remainder of specific information processing tasks, especially as they relate to the informatics of crowds.  And in this sense algorithms have significantly taken on what, at least since Arnold, has been one of culture’s chief responsibilities, namely, the task of “reassembling the social,” as Bruno Latour puts it—here, though, by discovering statistical correlations that would appear to unite an otherwise disparate and dispersed crowd of people.

I expect to post a complete draft of the piece on “Algorithmic Culture” to my project site once I’ve tightened it up a bit. Hopefully it will generate even more comments, questions, and provocations than the blog post that inspired the work initially.

In the meantime, I’d welcome any feedback you may have about the short excerpt appearing above, or on the talk if you’re going to be in Nashville this week.

The subtitle of this post ought to be “apparently,” since I have developing doubts about substituting digital surveillance systems and complex computer programs for the considered — humane — work of culture.

Case in point: about six weeks ago, Galley Cat reported on a new Kindle-related initiative called “popular highlights,”which Amazon.com had just rolled out onto the web for beta testing.  In a nutshell, Amazon is now going public with information about which Kindle books are the most popular, as well as which passages within them have been the most consistently highlighted by readers.

How does Amazon determine this?  Using the 3G connection built into your Kindle, the company automatically uploads your highlights, bookmarks, marginal notes, and more to its server array, or computing cloud.  Amazon calls this service “back up,” but the phrase is something of a misnomer.  Sure, there’s goodwill on Amazon’s part in helping to ensure that your Kindle data never gets deleted or corrupted.  By the same token, it’s becoming abundantly clear that “back up” exists as much for the sake of your convenience as it does for Amazon itself, who mines all of your Kindle-related data.  The Galley Cat story only confirms this.

This isn’t really news.  For months I’ve been writing here and elsewhere about the back up/surveillance issue, and I even have an academic journal article appearing on the topic this fall.  Now, don’t get me wrong — this is an important issue.  But the focus on surveillance has obscured another pressing matter: the way in which Amazon, and indeed other tech companies, are altering the idea of culture through these types of services.  Hence my concern with what I’m calling, following Alex Galloway, “algorithmic culture.”

In the old paradigm of culture — you might call it “elite culture,” although I find the term “elite” to be so overused these days as to be almost meaningless — a small group of well-trained, trusted authorities determined not only what was worth reading, but also what within a given reading selection were the most important aspects to focus on.  The basic principle is similar with algorithmic culture, which is also concerned with sorting, classifying, and hierarchizing cultural artifacts.

Here’s the twist, however, which is apparent from the “About” page on the Amazon Popular Highlights site:

We combine the highlights of all Kindle customers and identify the passages with the most highlights. The resulting Popular Highlights help readers to focus on passages that are meaningful to the greatest number of people.

Using its computing cloud, Amazon aggregates all of the information it’s gathered from its customers’ Kindles to produce a statistical determination of what’s culturally relevant. In other words, significance and meaningfulness are decided by a massive — and massively distributed — group of readers, whose responses to texts are measured, quantified, and processed by Amazon.

I realize that in raising doubts about this type of cultural work, I’m opening myself to charges of elitism.  So be it.  Anytime you question what used to be called “the popular,” and what is now increasingly referred to as “the crowd,” you open yourself to those types of accusations. Honestly, though, I’m not out to impugn the crowd.

To my mind, the whole elites-versus-crowd debate is little more than a red-herring, one that distracts from a much deeper issue: Amazon’s algorithm and the mysterious ways in which it renders culture.

When people read, on a Kindle or elsewhere, there’s context.  For example, I may highlight a passage because I find it to be provocative or insightful.  By the same token, I may find it to be objectionable, or boring, or grammatically troublesome, or confusing, or…you get the point.  When Amazon uploads your passages and begins aggregating them with those of other readers, this sense of context is lost.  What this means is that algorithmic culture, in its obsession with metrics and quantification, exists at least one level of abstraction beyond the acts of reading that first produced the data.

I’m not against the crowd, and let me add that I’m not even against this type of cultural work per se.  I don’t fear the machine.  What I do fear, though, is the black box of algorithmic culture.  We have virtually no idea of how Amazon’s Popular Highlights algorithm works, let alone who made it.  All that information is proprietary, and given Amazon’s penchant for secrecy, the company is unlikely to open up about it anytime soon.

In the old cultural paradigm, you could question authorities about their reasons for selecting particular cultural artifacts as worthy, while dismissing or neglecting others.  Not so with algorithmic culture, which wraps abstraction inside of secrecy and sells it back to you as, “the people have spoken.”

My inner distribution nerd was thrilled to discover (via José Afonso Furtado) Michael Carins’ recent reflections on the death of the international standard book number, or ISBN, over on his blog PersonaNondata.  The argument goes something like this.  Over the last several years there has been a noticeable movement away from the ISBN, particularly in the case of e-books.  Leading the way has been Amazon.com, which refuses to assign ISBNs to any of the Kindle books it sells.  With book digitization there has also tended to follow dis-aggregation, or the chopping up of books into smaller, component parts that can be sold separately.  How do you assign a single ISBN to what’s fast becoming an exploding whole?

Cairns clearly knows his stuff.  As a former President of Bowker, he was chin-deep in the trenches of the recent effort to rework the ISBN for the 21st century.  The result was the shift from a 10-digit to a 13-digit standard, which went into effect on January 1, 2007.  My question is this: is the ISBN still necessary?

Anyone who’s read The Late Age of Print will know that I do not ask this question lightly. I devote the better part of Chapter 3 to the ISBN’s history, and to tell you the truth, in the process of doing the research I developed something of a crush on this smart little product code.  Personally I’d be sad to see it go.  But as an historian of technology it seems clear that the ISBN has just about exhausted its usefulness.

It’s important to bear in mind what computing and online communications looked like when the ISBN was first conceived, back in the late 1960s.  Processing power was paltry by today’s standards.  Broadband was barely an inkling of an idea.  The ISBN was developed within the context of these technological constraints, as a concise and thus highly efficient way in which to convey extremely detailed information about the language, publisher, title, and edition of any given book.

Today computers are capable of processing much more complex data strings, which need not be limited to numerals or the occasional letter X.  Furthermore, broadband has resulted in much faster electronic communications and consequently obviates the need to “keep it simple” and to the point (Twitter notwithstanding).  In other words, the constraints under which the ISBN was created hardly apply today.

The ISBN was designed not only to facilitate “back-office” communications about books.  It was also designed to facilitate their distribution.  And in this respect Amazon’s move away from the ISBN with its Kindle editions is telling.  Time and again the company has shown that it, and only it, wants to control the distribution of Kindle books.  Indeed they are digitally rights managed so as to forestall their circulation beyond anyone besides the reader/customer/end-user/licensee (I’m not entirely sure what to call this person anymore).  Amazon is moving us away from an era of more or less unfettered book circulation, and its slow abandonment of the ISBN is a manifestation of this.

It’s also worth remembering that the ISBN grew up at a time when the book industry showed perhaps its sharpest division of labor.  There were authors, agents publishers, typesetters, printers, binders, distributors, booksellers, and certainly a whole host others all working in concert in disparate places on a single product.  Now consider Amazon. With Kindle the company effectively becomes an extension of the publisher, typesetter, printer, and binder, all while acting as book distributor and seller.  If Amazon has its way then we are likely to see a further breakdown in the book industry’s division of labor.  What’s the point of an industry Esperanto when centralization is fast becoming the order of the day?

Incidentally, this is precisely why the answer to my question, “Is the ISBN still necessary?” is still a “yes,” despite all that I have had to say about historical contexts and the like.  The ISBN was more than just a product code.  It was an accomplishment — a testament to an industry’s ability to achieve unity despite the pressures of competition, corporatization, and globalization.  Disturbingly, the waning of the ISBN signals the opposite trend: the growing hegemony of a single player who holds disproportionate sway over the industry as a whole.

Via Filed By and my good friend José Afonso Furtado’s Twitter Feed comes this fascinating Publishers Weekly story about Perseus Book Group and its BIG EXPERIMENT at BookExpo America 2009.  The crux of the matter is this: Perseus plans on publishing a 144-page book consisting of “sequels” to some of literature’s great opening lines — all within the span of 48 hours.

The title of the work — Book: The Sequel — clearly isn’t just about the content.  It’s as much if not more about the publishing industry and how it operates (or could operate), which is to say nothing of the existential crisis its main product — the book — finds itself in today.  What we have in Book: The Sequel is more than just print-on-demand, it’s essentially books, now!

I’m usually fairly circumspect of experiments like these.  Rarely are they particularly well thought through, and often they put far too much faith in simple, technological solutions or outcomes.  Not here.  Perseus proposes a remarkably holistic picture of what book publishing could be in the not-so-distant future — or later this week, if you want to get all “the future is now” about it.

First, the substance: crowdsourced content.  There already have been experiments in collaborative book writing, so in a sense what Perseus is doing is not altogether new.  Those who wish to contribute to the volume can log on to www.bookthesequel.com, where they can can pitch their own opening line sequels.  On the other hand, the Press’ experiment in crowdsourcing demonstrates one possible future function publishers may choose to take on.  That is, they may opt to become aggregators of decentralized information, as opposed to their simply remaining the gatekeepers of already centalized or unified information.  Perseus also plans on focus-grouping the cover designs using similar means, which is in keeping with my previous post on the marketing power of a site like Scribd.

Next, the product, which is multiple.  Perseus plans on releasing digital, audio, and online versions of Book: The Sequel, as well as a tangible, print-on-paper volume.  This is impressive.  Too often experiments in flash publishing result in only one of these — usually the e-edition and nothing more.  The looming test of the book industry’s mettle will be in how well it works — quickly and elegantly — across both analog and digital platforms.

Finally, the opportunities for post-publication interactivity.  Thus far publishing has done a fairly good job in recognizing the growing importance of author-audience interaction.  It has built ample infrastructure to support this.  But what the industry hasn’t caught on to well enough yet is the importance of decentralizing its social networks.  Online book marketing has been preoccupied with bringing audiences back again and again to the publishers’ or the authors’ websites.  This is understandable.  But we live in a time when conversations about culture happen all over the place, and increasingly on Facebook and Twitter.  It’s a testament to Perseus’ vision that it’s recognized how it need not try to control or consolidate the conversation about its book for that conversation to occur.

My only misgiving — and it is a significant one — about Book: The Sequel is that there appears to be no structure in place to compensate those who’ve donated their labor to create the book’s content.  This will have to change, even if it ultimately results in micro-payments to the authors (which, as Chris Anderson has shown, can add up in the long run).  Any book publishing business model that relies on crowdsourced content but that does not compensate the crowd for its initiative, wisdom, and goodwill surely will be unsustainable.

That said, Perseus plans on donating the profits of its grand experiment to the National Book Foundation. Who could have any truck with that?