Mindfulness as a Potential Buffer of Stereotype Threat for Underrepresented Minority Females in STEM
Bibliographic record
Abstract
Women comprise nearly half of the workforce, but are underrepresented in Science, Technology, Engineering, and Mathematics (STEM) occupations with women of color largely invisible across all levels of education in STEM. One likely reason for this is stereotype threat, a predicament whereby people feel at risk of conforming to stereotypes about their social group. Individuals who experience stereotype threat show performance decrements across a range of tasks, because their cognitive (working-memory) resources are directed toward overcoming the negative stereotype rather than toward completion of the task. To address a gap in the literature about the experiences of underrepresented minority women in STEM, this study examined whether a brief mindfulness intervention would counteract the effects of stereotype threat on underrepresented minority women’s math performance, as mindfulness can alleviate working-memory load. Participants were recruited via Qualtrics, randomly assigned into treatment or control groups, and completed a multi-step virtual study. They completed a math test, then either listened to a brief mindfulness audio recording or moved straight into the second math test, depending on their group. Participants in the stereotype threat group were primed for stereotype threat, and then completed the second math test. Participants in the control group completed the second math test without being primed. All participants then completed the Toronto Mindfulness Scale (TMS) to assess whether the mindfulness task was successful. The results were not statistically significant, likely due to the virtual nature of the study. However, the implications for practice, as well as gender and racial equality are invaluable.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".