Working together to address sexual misconduct in the Canadian Armed Forces
Bibliographic record
Abstract
LAY SUMMARY In 2015, the Canadian Armed Forces (CAF) implemented Operation HONOUR to eliminate sexual misconduct (SM) in the military. Sexual assault, inappropriate sexual behaviours, sexual harassment, and gender discrimination are all types of SM. Experiencing SM can result in depression, substance abuse, physical health problems, and even posttraumatic stress disorder (PTSD). Despite Operation HONOUR, SM still happens in the CAF. At this time, many groups are working together to address SM and to support those who have experienced SM. Canadian-based researchers, policy makers, military members, Veterans, and clinicians are collaborating to identify new approaches to training, culture change, research, and treatment relating to SM in the CAF. The end goal of working together is to minimize SM in the CAF and ensure the health and safety of all CAF members and Veterans.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.051 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".