Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the cultural aspect of policing, particularly as it relates to the role of gender, and proposes an alternative approach to addressing the culture of masculinity within policing. Design/methodology/approach – First, the author provides a brief overview of the nature of policing. This is followed by a review of the relevant literature on policing and gender and the implications for men, women, and police organizations of adhering to a militarized or hegemonic form of masculinity. Finally, the author discusses Ely and Myerson’s proposed theory for “undoing gender” and its relevance for policing. Findings – The findings of this paper suggest that the police culture continues to reinforce the masculine image of policing, thereby representing a significant barrier to the advancement of women. The findings also suggest that this barrier may be overcome through shared goals that advance collective well-being, definitions of competence linked to task requirements, and a learning orientation toward work. Originality/value – This paper makes an important contribution to the existing literature on gender and policing, as it specifically focusses on the cultural influences of masculinity and considers the structural, behavioral, and cultural changes required to create margins of safety for police officers to experiment with new behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.105 | 0.050 |
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".