Bibliographic record
Abstract
Purpose This study aims to examine gender issues in a sample of male and female police officers in Norway. Design/methodology/approach Three gender issues were considered: perceptions of equal opportunity, possible reasons for differences in male and female career opportunities, and experiences of sexual harassment. Data were collected from 766 police officers in Norway using anonymous questionnaires, a 62 percent response rate. Findings Female officers indicated significantly lower levels of equal opportunity perceptions, more reasons for career opportunity differences (particularly discrimination), and more sexual harassment than did male officers. Female officers reporting lower levels of equal opportunity perceptions were less job‐satisfied, more cynical, rated their quality of leadership lower and indicated more health complaints. Female officers experiencing more sexual harassment also indicated less job satisfaction. Finally, female officers offering more reasons for career differences (particularly discrimination) reported less job satisfaction, and lower professional efficacy. Research limitations/implications Future research needs to examine gender issues in policing in greater depth using qualitative methodology. Data collected used self‐reports ,raising the possibility of response set tendencies. Results may not generalize to other countries or other professions. Practical implications Suggestions for addressing gender issues in organizations are offered. Originality/value Provides current information on consequences of gender issues in policing in a cross‐cultural context.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".