How Does Industry Gender Ratio Affect Workplace Sexual Assault against Women? - A Panel Analysis in Canada
Bibliographic record
Abstract
Sexual assault against women has become a serious crime with a high incidence in Canada. As a form of sexual assault, workplace sexual assault not only does harm to women’s physical and mental health, but also to their career development and financial situation. Thus, to reduce and eliminate workplace sexual assault against women is of great significance. According to the sex-role spillover model by Gutek and Morasch and the integrated model by Fitzgerald and colleagues, the industry gender ratio, i.e. the proportion of male to female in industries, is a potential determinant to the occurrence of workplace sexual assault against women. However, a gap in the current knowledge of workplace sexual assault against women was revealed that no research has addressed relationships between industry gender ratio and workplace sexual assault in a Canadian context. This study wishes to find if how industry gender ratio affects workplace sexual assault against women. After reviewing the previous studies in workplace sexual assault and Canada’s policies against it, a panel data analysis using a Canadian nationwide survey dataset was conducted to explore the relationship between industry male share and female victim share of workplace sexual assault. The analysis found that on average a 100% increase in industry male share is significantly associated with a 4.8% decrease in female victim share of workplace sexual assault, holding age, province fixed effects, industry fixed effects and time fixed effects constant. This result suggests that industry gender ratio has a significant impact on workplace sexual assault against women in a Canadian context. With regard to the literature review and the empirical analysis, policies and strategies related to industry gender ratio are recommended to contribute to the reduction and elimination of workplace sexual assault in Canada.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".