Situational factors and police use of force across micro‐time intervals: A video systematic social observation and panel regression analysis
Bibliographic record
Abstract
Abstract The current study analyzes police use of force as a series of time‐bound transactions between officers, civilians, and bystanders. The research begins with a systematic social observation of use‐of‐force events recorded on police body‐worn cameras in Newark, New Jersey. Researchers measure the occurrence and time stamps for numerous participant physical and verbal behaviors. Data are converted into a longitudinal panel format measuring all observed behaviors in 5‐second intervals. Panel logistic regression models estimate the effect of each behavior on use of force in immediate and subsequent temporal periods. Findings indicate certain variables influence use of force at a distinct point in time, whereas others exert influence on use of force across multiple time periods. The most influential variables relate to authority maintenance theoretical constructs. This finding supports prior perspectives arguing that police use of force largely results from officer attempts to maintain constant authority over civilians during face‐to‐face encounters. Nonetheless, a range of additional variables reflecting procedural justice, civilian resistance, and bystander presence significantly affect when police use force during civilian encounters. Results provide nuance to theoretical frameworks considering use of force as resulting from the interplay between officer and civilian actions and reactions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".