Tomorrow’s Men Today: Canadian Men’s Insights on Engaging Men and Boys in Creating a More Gender Equal Future
Bibliographic record
Abstract
In 2018, the federal government put out a competitive bidding process to conduct a national research study with men to inform the development of a federal engagement strategy for men and boys that promotes gender equality and to develop a more nuanced understanding of masculinities in Canadian society by seeking lived experiences of men who resist gender-based hierarchies and prejudices. In November 2018, Shift was awarded the contract and designed a qualitative study to reveal the motivations and experiences of pro-feminist men currently engaged in gender equality work in Canada and to learn how we can attract, invite, encourage, and support other men and boys to get engaged and mobilized in this work. Thirty-three male-identified gender equality advocates were interviewed from coast to coast to coast. This research report highlights themes, lessons learned and recommendations on how we can better support male-identified gender equality and violence prevention advocates.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.073 | 0.023 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".