The influence of self-efficacy, interest, and stereotype threat on career intentions and choices related to math and science
Bibliographic record
Abstract
Women continue to be underrepresented in Science, Technology, Engineering and Mathematics (STEM) careers/sectors. Concurrently, negative stereotypes about women’s abilities to perform in STEM persists. This research examined whether gender stereotypes influence women’s STEM-related intentions and choices and the mediating influence of cognitive predictors based on the Social Cognitive Career Theory (SCCT; Lent, Brown, & Hackett, 1994). In total, 194 women from Ryerson University were randomly assigned to a stereotype threat (n =65), stereotype nullification (n = 65), or control condition (n = 64). Participants completed questionnaires assessing math self-efficacy, math and science interests and intentions, and a math/verbal choice task. In support of SCCT, math self-efficacy and math/science interests predicted math/science intentions and choice on the math/verbal test. Furthermore, “math identified” participants in the stereotype threat condition reported lower math/science intentions. This research has implications for current interventions designed to increase women’s participation and retention in STEM.
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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".