Ben Jones and Eduardo Mendieta (eds) (2021) The Ethics of Policing: New Perspectives on Law Enforcement
Bibliographic record
Abstract
By no small measure, quite the most thought-provoking book on policing in some time. Uppermost among the thoughts provoked: how many police officers, including those of senior rank, reach retirement age after a lifetime of police service without fully realizing what policing is or means? Those who do not understand how that is a question that can even be asked, probably would benefit significantly from reading this collection of powerful, often discomforting, essays. Their focus is policing in America today but reading about the lived experience of policing/being policed in Ferguson, Missouri, or Milwaukee, Wisconsin, one could just as easily be reading about Newham, London (UK); Redfern, Sydney (Australia); Fort McMurray, Alberta (Canada): the quest for lawful policing versus effective policing; uncertain conceptions of legitimate policing and professional norms; ‘rewards’ for ‘real’ police work that undermine reform and resolution; the intrinsic and systemic injustice of racial bias—topics that transcend national, regional, and local governance; topics that resonate, all be it with local nuance, everywhere.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".