Addressing ‘wicked complex problems’: Qualitative understandings of sexual violence prevention in male-dominated industries
Bibliographic record
Abstract
Globally there has been increased focus on the prevalence of sexual violence in workplaces, including the mining industry. In Western Australia, where this study is situated, this industry is a significant employer, predominantly male-dominated, and the prevalent use of fly-in-fly-out schedules can blur work and social life. Consequently, efforts to prevent and manage workplace sexual violence have become a priority, leading to the development and implementation of various strategies and resources. Qualitative interviews were conducted with 34 participants, including sexual violence prevention professionals (n = 16) and mining industry workers (n = 18). Reflexive thematic analysis identified systemic and behavioural considerations that may be transferrable to other male-dominated workplaces. Participants emphasised the importance of primary prevention training programmes that included engaging activities, realistic scenarios, careful use of language, humour and inspirational facilitators. Genuine consultation with workers is critical to ensure key messages are appropriately received. Prevention interventions also need to be supported by appropriate reporting mechanisms and support processes for victim/survivors. This research aims to provide an understanding of current sexual violence prevention initiatives within the Australian mining sector, offering recommendations for future approaches tailored to industries and contexts with similar dynamics.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".