Why do extracurricular activities prevent dropout more effectively in some high schools than in others? A mixed-method examination of organizational dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it