The Influence of Gender Role on Gender Segregation of STEM Majors in Chinese Universities
Bibliographic record
Abstract
Science and technology are the primary productive forces of a country.However, in today's STEM (Science, Technology, Engineering, and Mathematics) fields, gender segregation remains an issue.Half of the world's population is female, yet women face considerable barriers and are underrepresented in STEM education and occupations.This article mainly focuses on STEM education, to explore how gender role is shaped and reinforced in high school and how it leads to gender segregation of STEM majors in Chinese universities.The findings show that gender stereotype leads female students to devalue self-cognition and self-assessment and, as a result, they often underestimate their ability in STEM disciplines.Second, the educational policy in high school causes female students to prioritize liberal art subjects at the expense of natural science subjects.Third, high school curriculum, textbooks, and other teaching materials that contain gender bias and unhealthy teacher-student interactions reinforce the stereotype of gender role.Fourth, the decisions of majors are strongly affected by traditional Chinese culture that represents the preferences and career expectations for different genders.At the end of this article, implications will be provided.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".