Unleashing Latent Ability: Implications of Stereotype Threat for College Admissions
Bibliographic record
Abstract
Social-psychological research conducted over the past 15 years provides compelling evidence that pervasive psychological threats are present in common academic environments—especially threats that originate in negative intellectual stereotypes—and that these threats undermine the real-world academic performance of non-Asian ethnic minority students and of women in math and science. As a consequence, common measures of academic performance, including both grades and test scores, systematically underestimate the intellectual ability of ethnic minority students and of women in quantitative fields (Walton & Spencer, 2009 Walton, G. M. and Spencer, S. J. 2009. Latent ability: Grades and test scores systematically underestimate the intellectual ability of negatively stereotyped students. Psychological Science,, 20,: 1132–1139. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). We review evidence for these psychological threats, discuss their implications for the meaning and interpretation of common performance measures used in important admissions decisions, and address their implications for the efforts of colleges and universities to create positive academic environments that allow all students to thrive.
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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 | 0.035 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".