Bibliographic record
Abstract
The author develops the notion of “police/archives” based on his experience of trying to conduct research at the Toronto Police Museum. Drawing on Foucault, the author explores the reciprocal relationship between the police as archives and, especially, the archives as police. Another goal is to disentangle Foucault from discussions of “the Archive” as metaphor in both the archival literature and in queer theory. The author makes the case for a less metaphorical, more historicalmaterialist understanding of Foucault in and on archives.RÉSUMÉL’auteur développe la notion de « police/archives » à partir de ses tentatives de recherche au Toronto Police Museum. S’inspirant de Foucault, il explore la relation réciproque entre la police comme archives et surtout les archives comme police. Un autre de ses buts est de sortir Foucault des discussions portant sur « les Archives » comme métaphore, tant dans la littérature archivistique que dans la théorie queer. L’auteur explique le besoin d’une approche moins métaphorique et plus historicomatérialiste par rapport à notre entendement de Foucault, tant dans les archives qu’au sujet d’elles.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.684 | 0.489 |
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".