Bibliographic record
Abstract
The persistent underrepresentation of Black women in Science, Technology, Engineering and Mathematics (STEM) fields is largely attributed to their race and gender. With measures and interventions being continually undertaken to attain race and gender parity in STEM workforce, there is a lack of information science-based approaches in literature and practice. This might account for the less progress and increase recorded in the participation of Black women in the profession. This gap in STEM fields is an issue of national importance that seeks urgent solution. This paper aims to highlight the experiences of Black women in STEM, and implications for policy and practice. Les femmes noires en STIM: repenser le rôle des sciences de l'information comme voie d'accès à l'équité en STIM aux États-Unis RésuméLa sous-représentation persistante des femmes noires en sciences, technologie, ingénierie et mathématiques (STIM) est largement attribuée à leur race et à leur genre. Alors que des mesures et des interventions sont continuellement entreprises pour atteindre la parité entre les races et les genres au sein de la main-d'œuvre en STIM, les approches fondées sur les sciences de l'information sont insuffisantes dans la littérature et dans la pratique. Cela pourrait expliquer le peu de progrès et d'augmentation dans la participation des femmes noires à la profession. Ce fossé dans le domaine des STIM est une question d'importance nationale qui nécessite une solution urgente. Cet article vise à mettre en lumière les expériences des femmes noires en STIM et les implications pour la politique et la pratique. Mots-clésGenre; race; STIM; comportement informationnel
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".