Student Aid Reforms in Quebec: \nIs Changing the Clawback Rate Better \nthan Changing the Base Grant?
Bibliographic record
Abstract
Nous examinons deux réformes possibles au programme d'aide financière aux études: une réduction de la contribution étudiante ou une augmentation du seuil de revenu à partir duquel cette contribution s'applique. Nous présentons une analyse théorique et empirique des deux options. Nous montrons que toutes les deux réduisent les incitatifs au travail, bien que la réduction de la contribution étudiante ait moins d'impact. La réduction de la contribution étudiante cible davantage ceux dans le besoin, bien qu'elle accorde trop d'aide aux étudiants à revenu trop élevé. Nous estimons ainsi que la réduction de la contribution étudiante, si elle est limitée à un intervalle approprié de revenu, pourrait être la plus sensée. Abstract: We examine two ways through which student financial aid can be reformed: a cut in the rate at which the aid is clawed back with earned income or an increase in the threshold at which this clawback applies. We present a theoretical and empirical analysis of these options. We show that both reduce incentives to work, although the clawback rate does so less. Cuts to clawbacks also deliver a bigger boost to financial aid for those most in need, although they may benefit students higher in the income distribution. We argue that governments might consider a policy that reduces clawback rates, but within a reasonable range of earned income.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".