Chinese Female Students and the STEM Gender Gap: How Stereotype Threat and Expectancy Value Shape Performance and Engagement
Bibliographic record
Abstract
Globally, women have been underrepresented in science, technology, engineering, and mathematics (STEM) fields, and this is true in China. The current study seeks to identify factors that shape adolescent girls’ decision-making when deciding whether to pursue STEM studies and the barriers they face. The study likewise seeks to develop recommendations to encourage and empower girls to choose STEM. Data was collected from one-on-one interviews with six Chinese female international students: three in the STEM fields, and three in non-STEM fields at a comprehensive university in southwestern Ontario. The interviews explored the familial, educator, and peer influences that promoted or challenged gender stereotypes related to STEM. The data suggest that girls are less likely to enjoy or enroll in STEM classes if their parents and teachers promote negative stereotypes about STEM, offer disparaging criticisms of the girls’ STEM performance, and have different expectations based on gender. Inversely, girls are more likely to enroll in STEM if they have parents and teachers who prepare them for and encourage them to pursue STEM. This is likewise true in instances where parents objectively discuss and deconstruct sexist views of girls and STEM, and girls who have role models who work in STEM fields, whether those role models are male or female.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".