Cultural Infrastructure (Review of List Cultures by Liam Cole Young)
Bibliographic record
Abstract
On the face of it, there is something uniquely contemporary about the practices and procedures of listing. The present era might be variously characterized according to the ‘kill lists’ of drone warfare, the instructional lists of computational algorithms, the cultural rankings of the ‘best of’ list, or the ubiquitous clickbait ‘listicle’ that vies for our attention. Indeed it would seem that the politics and aesthetics of digital culture can be traced in the ever more visible proliferation of lists. Yet in List Cultures: Knowledge and Poetics from Mesopotamia to Buzzfeed, the first book by Canadian scholar Liam Cole Young, listing is shown to have been ‘a part of every new media ecology and its corresponding “flood” of information’ (14). Young, currently a lecturer in the School of Journalism and Communication at Carleton University, Ottawa, argues that the cultural technique of listing is ancient, and provides the foundation of administrative and organizational power from which both state and corporate institutions have emerged. Moreover, quite apart from any apparent visibility, he explains how lists are fundamentally recessive, and why they should be understood as operational forms that provide the infrastructural background to human society, mediating our knowledge of the world. For Young, ‘quotidian forms like the list are heuristics for understanding such “civilizational” questions of order, knowledge, and being’ (49).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".