“You Don’t Need a Rocket Scientist to Figure Out What Could Happen”: Reasoning Practices in Police Use of Force Trials
Bibliographic record
Abstract
Abstract Trials involving police as defendants are rare but are significant events that give insight into police violence and its adjudication. This article explores the reasoning practices through which court actors navigate the disjunctive accounts created by competing claims of “what happened” in a police shooting. The data is drawn from trial testimony of officers and “use of force experts” in police deadly force cases in the United States. We focus on use of force experts who use a veneer of science and police logic to assert particular visions of officer “reasonableness.” We suggest that the systems of reasoning that lawyers and witnesses use in these cases create accounts of police violence that conflict with mundane reasoning and challenge credibility. We show that the proliferation of different reasoning practices and the elaboration of a “police logic” serve to insulate officers from criticism and accountability—albeit, not always successfully.
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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 | 0.066 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".