Bibliographic record
Abstract
This paper considers the history and politics of ‘police data.’ Police data, I contend, is a category of endangered data reliant on voluntary and inconsistent reporting by law enforcement agencies; it is also inconsistently described and routinely housed in systems that were not designed with long-term strategies for data preservation, curation or management in mind. Moreover, whereas US law enforcement agencies have, for over a century, produced and published a great deal of data about crime, data about the ways in which police officers spend their time and make decisions about resources—as well as information about patterns of individual officer behavior, use of force, and in-custody deaths—is difficult to find. This presents a paradoxical situation wherein vast stores of extant data are completely inaccessible to the public. This paradoxical state is not new, but the continuation of a long history co-constituted by technologies, epistemologies and context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.013 | 0.068 |
| Scholarly communication | 0.026 | 0.062 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".