Bibliographic record
Abstract
Abstract In academic and organizational domains, performance measures are often used to assess achievement or aptitude. When certain groups of people systematically underperform on such measures, a common interpretation is that the groups differ in inherent ability. However, social psychological research over the past 15 years has documented a phenomenon called stereotype threat whereby subtle situational reminders of negative stereotypes can stifle the performance of those who are targeted by them. In this article, we review research aimed at understanding the sequence of cognitive and affective processes that underlie these situationally‐induced performance impairments. We review evidence that being the target of negative stereotypes cues self‐uncertainty and a physiological stress response, engages more explicit monitoring of one’s performance, and efforts to regulate unwanted negative thoughts and feelings. Alone or in concert, these extra‐task processes hijack cognitive resources needed for successful performance. Armed with the knowledge of these mediating mechanisms, we then review evidence from both field and laboratory based research demonstrating that gender and racial gaps in achievement can be alleviated if not eliminated through creative and often subtle interventions that diffuse the pernicious effects that stereotypes can have.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".