Bibliographic record
Abstract
Abstract This article analyzes and explores law enforcement officer identity, arguing that officer identity is less stable than previously realized and has the ability to evolve over time. In interviewing 29 law enforcement officers from a rural, small-town sheriff’s department in the Western United States, I found that specific identities emerged from narratives about culpability. Applying critical discourse analysis to culpability narratives (narratives in which officers place blame for their actions on the public or themselves) uncovered a traditional “tough guy” identity or a non-normative “human” identity. When identities flex, indexical links are altered such that they evolve and deepen the pool of potential identities available for officers to draw on. The processes that establish and make officer identities performable and viable can metamorphose over time, bringing about new police discourses and identities. As more idiosyncratic or non-normative identities, like the “human” identity seen here, are performed and circulated, they have the ability to compel change within policing discourses and cultures, potentially paving the way for police reform.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".