Lead Them to Water and Pay Them to Drink: An Experiment with Services and Incentives for College Achievement
Bibliographic record
Abstract
High rates of attrition, delayed completion, and poor achievement are growing concerns at colleges and universities in North America.This paper reports on a randomized field experiment involving two strategies designed to improve these outcomes among first-year undergraduates at a large Canadian university.One treatment group was offered peer advising and organized study group services.Another was offered substantial merit-scholarships for solid, but not necessarily top, first year grades.A third treatment group combined both interventions.Service take-up rates were much higher for students offered both services and scholarships than for those offered services alone.Females also used services more than males.No program had an effect on grades for males.However, first-term grades were significantly higher for females in the two scholarship treatment groups.These effects faded somewhat by year's end, but remain significant for females who planned to take enough courses to qualify for a scholarship.There also appears to have been an effect on retention for females offered both scholarships and services.This effect is large enough to generate an overall increase in retention.On balance, the results suggest that a combination of services and incentives is more promising than either alone.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".