Have We Left the Boys Behind? Addressing the Decline of Male Participation in Higher Education in Quebec
Bibliographic record
Abstract
Generally, research related to equity, diversity, and inclusion (EDI) focuses on people with disabilities, ethnic or cultural minorities, and sexual or gender minorities. It follows in the tradition of initiatives that have progressively enabled women to take their place in society, expanding these efforts to include a broader range of underrepresented groups. Men who do not live with disabilities and who are not part of a minority group are therefore often overlooked in these studies. Yet, available data show that in higher education, they have not been the majority among students for quite some time. This highlights the need to examine what is happening earlier in the educational pathway, as academic delays and dropouts often begin in childhood. This invites us to question the potential role that certain technologies could play in supporting boys’ academic success. Additionally, we will see that income inequalities disadvantaging women may also help explain differences in the return on investment in higher education between men and women—underscoring the importance of considering the relationships between traditionally studied minority groups and others who may not appear marginalized at first glance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".