Bibliographic record
Abstract
The proliferation of video evidence has opened new opportunities to scrutinize police use-of-force incidents, including for observational and interactionist researchers. While these opportunities are exciting, it is necessary, in our view, to reflect on the lens through which these analyses occur. We examine how Harold Garfinkel’s notions of unique adequacy and accounts can be applied to video data from use-of-force incidents. To demonstrate, we analyze a video of a violent arrest that occurred in Kitchener, Ontario, Canada, as well as transcripts of a subsequent civil action against the arresting officer, claiming damages for excessive force not in the arrest as a whole but for one specific action within the arrest – a punch to the back of the head of the plaintiff. We argue it may be politically advantageous to employ theoretical frames to a video analysis that are not germane to police practice itself (i.e. “procedural justice” or sociology of emotions) but that doing so also deviates from developing an understanding of the lived experiences of those depicted in the scene or the situated practices of making sense of those scenes as a course of adjudicative work. We take it that these would be the objective of a truly interactionist sociology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".