Where do we go from here? Current issues in police work
Bibliographic record
Abstract
The symposium includes four empirical papers that explore how police officers think and feel about their work and their identities. These studies address the implications of traumatic incidents at work on mental health, how work and parenthood interact to increase stress for individuals, how training can influence ones’ professional identity, and perceptions of one’s occupational identity and its implications for workers’ behaviors. Each study is qualitative, exploring meaning making processes as they are subjectively experienced in context. Creating safe relational spaces for emotional processing in police work Presenter: Kimberly Rocheville; Boston College From brave enforcer to trusted protector: Role evolution and procedural justice training Presenter: Rodrigo Canales; Yale U. Presenter: Jessica Zarkin; Cornell U. Presenter: Lluvia Gonzalez; Innovations for Poverty Action Canadian Police mothers and the boys club: Implications of the combined challenges of work and home Presenter: Debra Langan; Wilfrid Laurier U. Presenter: Carrie Sanders; Wilfrid Laurier U. Police officers' occupational divide Presenter: Elizabeth Hood; Boston College Presenter: Jacqueline N Hood; U. of New Mexico Presenter: Lyndon Earl Garrett; Boston College
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.034 | 0.043 |
| Scholarly communication | 0.034 | 0.044 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".