archive.Joe Lamantia.com

Looks like Dave Sifry at Technorati has drunk the Clay Shirky Koolaid on tagging and social bookmarking. Here’s something from Dave’s posting State of the Blogosphere, August 2005, Part 3: Tags, that shows he’s clearly joined the academy of received ideas.
“Unlike rigid taxonomy schemes that many people dislike using, the ease of tagging for personal organization with social incentives leads to a rich and discoverable system, often called a folksonomy. Intelligence is provided by real people from the bottom-up to aid social discovery. And with the right tag search and navigation, folksonomy may outperform more structured approches to classification, as Clay Shirky points out…”

I’m disappointed to see this. The quality level of Shirky’s thinking and writing related to tagging is generally low; too often he’s so completely off the mark with much of what he’s said about tagging, social bookmarking, and categorization in general that his main contribution is in lending a certain amount of attention by virtue of name recognition to a subject that used to be arcane.

There’s little need to rehash the many, many individual weaknesses in Shirky’s writings, just one example of which is his establishment of a false dichotomy separating structured categorization systems and social tagging practices. Broadly, his approach and rhetoric show strong influence from anarchism, and utopian social theory.

From Shirky:
“There is no fixed set of categories or officially approved choices. You can use words, acronyms, numbers, whatever makes sense to you, without regard for anyone else’s needs, interests, or requirements.”
Further, “…with tagging, anyone is free to use the words he or she thinks are appropriate, without having to agree with anyone else about how something “should” be tagged.”

Building back on the criticique of computerization, it’s clear that Shirky uses rhetorical strategies and positions from both technological utopianism and anti-utopianism.

Here’s Professor Rob Kling on technological utopianism:
“Utopian images are common in many books and articles about computerization in society written by technologists and journalists. I am particularly interested in what can be learned, and how we can be misled, by a particular brand of utopian thought — technological utopianism. This line of analysis places the use of some specific technology, such as computers, nuclear energy, or low-energy low-impact technologies, as key enabling elements of a utopian vision. Sometimes people will casually refer to exotic technologies — like pocket computers which understand spoken language — as “utopian gadgets.”

Technological utopianism does not refer to these technologies with amazing capabilities. It refers to analyses in which the use of specific technologies plays a key role in shaping a benign social vision. In contrast, technological anti-utopianism examines how certain broad families of technology are key enablers of a harsher and more destructive social order.”

That Shirky would take speak from this standpoint is not a surprise; he’s identified as a “Decentralization Writer/Consultant” in the description of his session “Ontology is Overrated: Links, Tags, and Post-hoc Metadata” at etech, and it’s clear that he’s both technologist and a journalist, as Kilng identifies.

Regardless of Shirky’s bias, there is a bigger picture worth examining. Tagging or social bookmarking is one potential way for the community of social metadata system users to confront problems of individual and group information overload, via a collective and nominally unhierarchical approach to the emergent problem of information management across common resources (URIs).

Found via via, orig­i­nat­ing in an arti­cle titled Social Infor­mat­ics: Overview, Prin­ci­ples and Oppor­tu­ni­ties from the ASIST Bul­letin spe­cial issue on Social Infor­mat­ics, which, inci­den­tally is one of those very inter­est­ing dis­ci­plines I don’t have enough time to keep up with, but that has much to offer prac­tic­ing infor­ma­tion archi­tects.
On com­put­er­i­za­tion, Sawyer says, “Com­put­er­i­za­tion, to para­phrase soci­ol­o­gist Bev­erly Bur­riss, is the imple­men­ta­tion of com­put­er­ized tech­nol­ogy and advanced infor­ma­tion sys­tems, in con­junc­tion with related socioe­co­nomic changes, lead­ing to a fun­da­men­tal restruc­tur­ing of many social orga­ni­za­tions and insti­tu­tions.“
Add in a client man­age­ment clause, and this is essen­tially my job descrip­tion as an archi­tect / designer / cre­ator of infor­ma­tion envi­ron­ments that solve busi­ness prob­lems. I don’t know Bur­riss’ work — does any­one else?
Directly address­ing the role of a con­structed prob­lem Sawyer says, “…social infor­mat­ics is problem-oriented. This work is defined by its inter­est in par­tic­u­lar issues and prob­lems with com­put­er­i­za­tion and not by its adher­ence to cer­tain the­o­ries or par­tic­u­lar meth­ods (as is oper­a­tions research).“
In what looks like a neatly phrased snap­shot of user research, Sawyer says, “The strong empir­i­cal basis of social infor­mat­ics work, how­ever, is com­bined with both method­olog­i­cal and the­o­ret­i­cal plu­ral­ity. Social infor­mat­ics work typ­i­cally includes an array of data col­lec­tion approaches, sophis­ti­cated large-scale analy­ses and com­plex con­cep­tu­al­iza­tions.“
Here’s a longer excerpt:
The Com­mon Find­ings of Social infor­mat­ics
More than 30 years of care­ful empir­i­cal research exists in the social infor­mat­ics tra­di­tion. As noted, this work is found in a range of aca­d­e­mic dis­ci­plines, reflects a mix of the­o­ries and meth­ods, and focuses on dif­fer­ent issues and prob­lems with com­put­er­i­za­tion. Here I high­light five obser­va­tions that are so often (re)discovered that they take on the notion of com­mon find­ings rel­a­tive to com­put­er­i­za­tion.
1. Uses of ICT lead to mul­ti­ple and some­times para­dox­i­cal effects. Any one ICT effect is rarely iso­lat­able to a desired task. Instead, effects of using an ICT spread out to a much larger num­ber of peo­ple through the socio-technical links that com­prise con­text. An exam­i­na­tion of this larger con­text often reveals mul­ti­ple effects, rather than one all-encompassing out­come, and unex­pected as well as planned events. For exam­ple, peer-to-peer file shar­ing may help some musi­cians and hurt oth­ers.
2. Uses of ICT shape thought and action in ways that ben­e­fit some groups more than oth­ers. Peo­ple live and work together in pow­ered rela­tion­ships. Thus, the polit­i­cal, eco­nomic and tech­ni­cal struc­tures they con­struct include large-scale social struc­tures of cap­i­tal exchange, as well as the microstruc­tures that shape human inter­ac­tion. An exam­i­na­tion of power often shows that a system’s imple­men­ta­tions can both rein­force the sta­tus quo and moti­vate resis­tance. That is, the design, devel­op­ment and uses of ICTs help reshape access in unequal and often ill-considered ways. Thus, course man­age­ment sys­tems may pro­vide added ben­e­fits to some stu­dents, put added pres­sure on some fac­ulty and allow some admin­is­tra­tors to use the sys­tem to col­lect addi­tional evi­dence regard­ing the per­for­mances of both stu­dents and fac­ulty.
3. The dif­fer­en­tial effects of the design, imple­men­ta­tion and uses of ICTs often have moral and eth­i­cal con­se­quences. This find­ing is so often (re)discovered in stud­ies across the entire spec­trum of ICTs and across var­i­ous lev­els of analy­sis that igno­rance of this point bor­ders on pro­fes­sional naïveté. Social infor­mat­ics research, in its ori­en­ta­tion towards crit­i­cal schol­ar­ship, helps to raise the vis­i­bil­ity of all par­tic­i­pants and a wider range of effects than do other approaches to study­ing com­put­er­i­za­tion. For exam­ple, char­ac­ter­iz­ing errors in diag­nos­ing ill­nesses as a human lim­i­ta­tion may lead to the belief that imple­ment­ing sophis­ti­cated computer-based diag­nos­tic sys­tems is a bet­ter path. When these sys­tems err, the ten­dency may be to refo­cus efforts to improve the com­put­er­ized sys­tem rather than on bet­ter under­stand­ing the processes of triage and diag­no­sis.
4. The design, imple­men­ta­tion and uses of ICTs have rec­i­p­ro­cal rela­tion­ships with the larger social con­text. The larger con­text shapes both the ICTs and their uses. More­over, these arti­facts and their uses shape the emer­gent con­texts. This can be seen in the micro-scale adap­ta­tions that char­ac­ter­ize how peo­ple use their per­sonal com­put­ers and in the macro-scale adap­ta­tions evi­dent in both the evolv­ing set of norms and the chang­ing designs of library automa­tion sys­tems. Library automa­tion is not sim­ply about recent devel­op­ments of appli­ca­tions with sophis­ti­cated librar­i­an­ship func­tion­al­ity; it is also about patrons’ dif­fer­en­tial abil­i­ties to use com­put­ers, library bud­get pres­sures, Inter­net access to libraries and the increas­ing vis­i­bil­ity of the Inter­net and search­ing.
5. The phe­nom­e­non of inter­est will vary by the level of analy­sis. Because net­works of influ­ence oper­ate across many dif­fer­ent lev­els of analy­sis, rel­e­vant data on com­put­er­i­za­tion typ­i­cally span for­mal and infor­mal work groups; for­mal orga­ni­za­tions; for­mal and infor­mal social units like com­mu­ni­ties or pro­fes­sional occupation/associations; groups of orga­ni­za­tions and/or indus­tries; nations, cul­tural groups and whole soci­eties. This com­mon find­ing is exem­pli­fied by the tremen­dous pos­i­tive response by younger users to peer-to-peer file shar­ing, the absolute oppo­site response by music indus­try lead­ers and the many approaches taken by orga­ni­za­tional and civic lead­ers regard­ing the legal­i­ties and responses to use.

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