Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite progress made toward increasing women's interest and involvement in science, technology, engineering, and math (STEM), women continue to be underrepresented and experience less equity and inclusion in some STEM fields. In this article, I review the psychological literature relevant to understanding and mitigating women's lower fit and inclusion in STEM. Person-level explanations concerning women's abilities, interests, and self-efficacy are insufficient for explaining these persistent gaps. Rather, women's relatively lower interest in male-dominated STEM careers such as computer science and engineering is likely to be constrained by gender stereotypes. These gender stereotypes erode women's ability to experience self-concept fit, goal fit, and/or social fit. Such effects occur independently of intentional interpersonal biases and discrimination, and yet they create systemic barriers to women's attraction to, integration in, and advancement in STEM. Dismantling these systemic barriers requires a multifaceted approach to changing organizational and educational cultures at the institutional, interpersonal, and individual level.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 it