Contextualizing and defining de-escalation in policing
Bibliographic record
Abstract
In light of increasing concerns about use of force, one of the most prominent recommendations for improving policing is ‘de-escalation’. However, despite the hype surrounding de-escalation, a clear definition still is lacking. At the same time, debates about de-escalation have not been grounded in, or informed by, police practice. The lack of a solid definition is problematic in practice, as police struggle with potentially conflicting considerations. This study utilizes interviews with use of force experts and a survey of frontline officers to produce a better understanding of de-escalation. The results highlight the importance of nuance in police-public interactions, which are highly variable. In particular, de-escalation cannot be conceptualized as a one-size-fits-all solution capable of solving all elevated police-citizen interactions. A practical definition of de-escalation must also acknowledge that force may sometimes be required. The perspective that use of force is incompatible with de-escalation does not reflect the realities of policing.
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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".