Who Am I? Who Are We? Exploring the Factors That Contribute to Work-Related Identities in Policing
Bibliographic record
Abstract
Abstract Using social identity theory, this study examines the conditions under which police officers become attached (or not) to their organization and to their work, and whether one’s sex influences these relationships. Through an analysis of secondary survey data collected from a large Canadian police organization, the study found that fair treatment and psychological safety were significantly related to officers’ identification with their organization, and in turn, their work. The findings also demonstrated that when officers perceived their workplace as a masculinity contest, they were less likely to identify with their organization. Additionally, perceptions of a masculinity contest were associated with a greater likelihood that officers reported lower levels of psychological safety, and this effect was more significant for female officers. While women overall were no less likely than men to be attached to their organization or their occupational role, women who perceived their workplace as psychologically less safe reported lower levels of identification. The study also found that race and level within the organization may have a greater effect than sex on work-related identification. Overall, the study makes a significant contribution to the nascent literature on work-related identification and policing, as well as to the body of research on women in policing.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".