Exploring the Association of Sex and Majoring in Science
Bibliographic record
Abstract
Exploring the Association of Sex and Majoring in ScienceOne consequence of gender socialization is that different attitudes, behaviors, and aspirations are socially constructed as appropriate for men and women.It is no surprise, then, that arguments about gender socialization are widely used by researchers who study sex differences in those individuals who major in science (Etzkowitz, Kemelgor, & Uzzi, 2000; Mcllwee & Robinson, 1992; Valian, 1999).We build on this work and use the conceptual model presented in Figure 1 to explore whether gender socialization and its products, gender roles and gender stereotypes, mediate the relationship between sex and majoring in science.HI and H5 specify the total and residual sex effects respectively.H2, H3, and H4 use theoretical and empirical arguments about gender socialization to identify three sets of factors that may link sex to majoring in science and thus help account for the gender gap in science.Hypotheses 1.There is a negative relationship between being female and the likelihood of being a science major.2. Being female reduces the likelihood of being a science major because social constructions of women and science associated with traditional gender roles contribute to the underrepresentation of women in science by identifying a male breadwinner and a female homemaker and by sustaining the social construction of science as a male field (Etzkowitz et al, 2000; Rolin, 2001; Rosser & Zieseniss, 2000; Schiebinger, 1999; Tonso, 1999; Valian, 1999).3. Being female reduces the likelihood of being a science major because it is associated with lower levels of high school science and mathematics preparation, which is necessary for pursuing science in university (Betz, 1997; Leslie, McClure, & Oaxaca, 1998; Sax, 1994; Yauch, 1999).4. Being female reduces the likelihood of being a science major because it is associated with lower levels of mathematics self-efficacy, less interest in science and less encouragement to pursue science in university.Mathematics self-efficacy, interest in science, and encouragement to pursue science are all positively associated with majoring in science (Betz, 1997;
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".