Advancing police use of force research and practice: urgent issues and prospects
Bibliographic record
Abstract
Leading police scholars and practitioners were asked to reflect on the most urgent issues that need to be addressed on the topic of use of force. Four themes emerged from their contributions: use of force and de‐escalation training needs to improve and be evaluated; new ways of conceptualizing use of force encounters and better use of force response models need to be developed; the inequitable application of force, and how to remediate biases, needs to be more fully understood; and misconceptions about police use of force need to be identified and corrected. The highlighted topics serve as an agenda for future research. Such research should provide greater insight into when, where, and why force is used by police officers, and how it can be applied appropriately. If implemented, the practical recommendations included in the contributions should have a positive impact on police performance, public trust and confidence in the police, and citizen and officer safety.
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 | 0.306 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.028 | 0.050 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".