Can Police Officers Foresee the Future? Predicting Outcomes from Thin Slices of Police-Public Encounters
Bibliographic record
Abstract
Thin slice studies are studies that examine judgments based on brief exposure to expressive behaviours or still images.Only one previous study has examined the prediction of outcomes within a law enforcement context from thin slices of a police-public encounter, and it demonstrated that experienced officers outperformed less experienced officers in terms of the quality, appropriateness, and accuracy of their predictions (Suss & Ward, 2012).The present study extends this research by examining how a range of factors -including operational years of experience and training, familiarity with the encounter, confidence in the prediction, and thin slice length -impact prediction accuracy.Participants with varying levels of police experience and training were recruited.Participants viewed 16 randomly ordered videos (half of these were 10 seconds and half were 30 seconds in length) depicting a thin slice of a police-public encounter.After each video, the participant was asked to predict whether the subject would harm or attempt to harm the officer(s).My results demonstrated that higher levels of training, greater familiarity, and greater confidence in one's predictions was associated with greater odds of providing an accurate response; operational years of policing experience was not associated with this outcome.My results also demonstrated that most of these variables' relationships with prediction accuracy disappear when examining longer thin slices (i.e., 30-second videos), and have slightly larger effects when examining shorter thin slices (i.e., 10-second videos).Finally, specialized police training, years of experience, and familiarity were, in turn, found to predict greater confidence in one's predictions.These findings and their implications are discussed.Keywords: anticipation, outcome prediction, thin slice, police-public encounter, expertise, training, schema, use-of-force I would also like to extend my gratitude to the police collaborators who assisted me with developing the concept for this project, which I was fortunate to be able to see grow and make my own.I am also thankful for their help with participant recruitment,
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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 | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".