Work intensity and non-completion of university: longitudinal approach and causal inference
Bibliographic record
Abstract
Researchers focused upon the work–dropping out connection tend to show a U-shaped relationship between the likelihood of dropping out and the number of hours worked outside school, with a higher exit rate for both non-working students and for students whose working hours pass a critical threshold. Yet the data typically used by these researchers are drawn mainly from cross-sectional surveys, and as a result does not allow for any causal interpretation. The present article uses an event history analysis of Canadian longitudinal data covering seven years of a cohort, and offers original findings on the causal work–dropping out relationship at the university level. We find evidence showing that the evolution of the exit rates and the factors influencing the decision to quit a particular university programme differ substantially between students who want to enrol in another programme and those who do not. For the latter, we observe a critical threshold of 24 h of work, beyond which negative effects in terms of non-completion start to appear. We find no negative effects arising from not working vs. working a few hours. Our findings thus tend to show that the higher exit rate among non-working students evidenced in cross-sectional data should be attributed to the fact that academic difficulties cause some potential university dropouts to stop working and to devote more time to school.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".