Calling in All Men: 26 Recommendations for Engaging and Mobilizing Men to Prevent Violence and Advance Equity
Bibliographic record
Abstract
The purpose of the Calling In Men research project is to synthesize evidence-informed primary prevention approaches that engage and mobilize men to prevent and disrupt violence and inequalities, and to share these findings with those funding and working with men and male-identified people in Canada. As part of this project, nine rapid evidence reviews were conducted on promising approaches to motivating and engaging men in violence prevention and gender equality efforts. This report draws on findings from each of the rapid reviews to provide a high-level synthesis of emergent evidence for what works to engage and mobilize men to prevent violence and promote gender justice, equality, diversity, and inclusion. It includes a series of recommendations that were developed for a range of stakeholders, including governments, funders, researchers/evaluators, and practitioners. The findings also provide the foundation for identifying gaps in the field and formulating recommendations for the type and level of research, funding, learning, and action needed to make further progress in these areas.
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 | 0.163 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".