Why do extracurricular activities prevent dropout more effectively in some high schools than in others? A mixed-method examination of organizational dynamics
Bibliographic record
Abstract
This study describes policies and practices implemented in 12 high schools (Quebec, Canada) that more or less effectively leveraged extracurricular activities (ECA) to prevent dropout among vulnerable students. Following an explanatory sequential mixed design, three school profiles (Effective, Ineffective, and Mixed) were derived based on quantitative student-reported data. Qualitative interviews with frontline staff revealed that in Effective schools, ECA had a unique overarching goal: to support school engagement and perseverance among all students, including vulnerable ones. Moreover, in these schools staff had access to sufficient resources—human and material—and implemented inclusive practices. In Ineffective schools, ECA were used as a means to attract well-functioning students from middle-class families, and substantial resources were channeled toward these students, with few efforts to include vulnerable ones. Schools with a Mixed profile had both strengths and weakness. Recommendations for school-level policies that bolster ECA’s ability to support students’ perseverance are provided.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".