A scalable goal-setting intervention closes both the gender and ethnic minority achievement gap
Bibliographic record
Abstract
Abstract The gender and ethnicity gap in academic achievement constitutes one of today’s key social problems. The current study, therefore, assessed the effects of a brief, evidence-based online intervention aimed at enhancing goal-directed conceptualization and action among first year college students (N=703) at a large European business school. The academic performance of these students was contrasted with that of three pre-intervention control cohorts (N=896, 825 and 720), with particular attention paid to the role of gender and ethnicity. The intervention boosted academic achievement and increased retention rates, particularly for ethnic minority and male students (who had underperformed in previous years). The gap in performance between men and women, and for ethnic minorities versus nationals, became considerably smaller within the intervention cohort. After Year 1, the gender gap closed by 98%, and the ethnicity gap by 38% (rising to 93% after the second year). All groups in the intervention cohort performed significantly better than control cohorts, but the effect was particularly large for males and ethnic minorities. The increase in performance was largest for ethnic minority males: they earned 44% more credits, and their retention rate increased 54%. Overall, the results indicate that a comprehensive goal-setting intervention implemented early in students’ academic careers can significantly and substantially reduce gender and ethnic minority inequalities in achievement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".