School Mobility and High School Dropout: Seasonal and Developmental Timing Matters
Bibliographic record
Abstract
Non-promotional school changes are fairly common, and although most mobile students successfully adjust to new peers, routines, and teachers, school mobility can sometimes indicate risk of disengagement and even dropout. To identify which mobile students are at risk and in need of support, it is important to differentiate when mobility may pose a threat and when it does not. The goal of this study was to examine the role of temporality in the relationship between non-promotional school changes and high school dropout, in a sample of N = 545 secondary school students (52% boys; Mage = 16.3 years) followed over a 6-month period. Participants were recruited in 12 socioeconomically disadvantaged public secondary schools with high dropout rates in Quebec (Canada). Logistic regression analyses (taking into account key potential confounding variables) revealed that non-promotional secondary school changes were associated with dropout, but only when they occurred during school years or in later secondary grades, and not when they occurred between school years (i.e., during the summer break) or in early secondary grades. These findings indicate that non-promotional school changes occurring at certain key time points are clear indicators of increased risk of high school dropout. Students who experience such changes would benefit from targeted support to help them integrate into their new school and cope with other problems often associated with mobility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".