Do equity, diversity and inclusion (EDI) requirements change student political attitudes?
Bibliographic record
Abstract
Over the past few years, equity-diversity-inclusion (EDI) requirements have proliferated in higher learning institutions. What impact have they had on students? This paper leverages a unique opportunity to evaluate the effect of an EDI requirement at a large public university: taught by the same instructor over the course of four quarters in three years, the course administered short anonymous surveys to students at the beginning and at the end of each term. These surveys measured student political attitudes such as preferences toward refugee admissions and affirmative action. Repeated surveys and repeated quarters allow us to evaluate changes in student attitudes during each quarter and averaged over three years. Results indicate an increase in inclusionary attitudes driven largely by students at the lowest baseline levels. Further analyses allow us to rule out social desirability bias, and suggest one possible mechanism: participation in small-group peer discussions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.025 |
| 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".