Causes and solutions for the high male student dropout rate in Quebec: a research synthesis
Bibliographic record
Abstract
In this thesis, I explore the reasons behind the persistent high male student dropout rate in Quebec, as well as evidence for approaches to reduce it. To do so, I reviewed and analyzed fifty-eight documents, scholarly articles, and periodicals. Causes include a number of push and pull factors, such as social conditioning brought upon by gender norms and stereotypes, lack of male student engagement and motivation due to irrelevant curriculum materials, school environment and discipline policies, draw to the labour market, the impact of low socioeconomic status, gaps in literacy due to less attention in earlier grades, and lack of positive academic male role models. Research also involved synthesizing the solutions proposed in the literature: intervention programs, more diverse extracurricular activities, supporting student autonomy and the development of a growth mindset, and adopting frameworks where schools work with the community and parents to encourage students academically. These recommendations were built upon in a scholarly discussion, taking into account recent Quebec education policy, to derive solutions relevant to this province. Particular solutions discussed include a large-scale early intervention program that supports literacy, socioemotional skills, and an awareness of gender stereotypes, as well as a modification to the Work-Oriented Training Path so that all high school students interested in working can do so without jeopardizing their education
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".