‘I Clocked You Going 50 In a 25’: A Discourse-Based Critique Of Police Procedural Justice Research Through A Sequential Exploration Of ‘Voice’ And Excuses In Traffic Encounters
Bibliographic record
Abstract
Abstract The police in the United States typically pull over about 19 million drivers a year for routine violations such as speeding and running a stop sign. The verbal exchanges that occur during traffic encounters embody one of the ideal principles of procedural justice: giving citizens an opportunity to speak (voice) before a decision is made. The accounts and excuses that drivers articulate represent the logical outcome of opportunities provided to drivers to explain the reason for their legal violations. This paper examines the accounts and excuses that drivers proffer during routine traffic encounters. The findings indicate that drivers’ responses to police solicitation of accounts fall into three types: remaining ‘silent’ during encounters and forgoing an opportunity to voice their concerns; crafting excuses and apologies in response to the announcement of an infraction; and denial of knowledge. The implications for police procedural justice are discussed.
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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 | 0.046 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.029 | 0.149 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".