Merging dualities: How convergence points in art and science can (re)engage women with the STEM field
Bibliographic record
Abstract
Abstract: How can the interweaving of knowledge silos help to engage girls who are becoming disinterested in science? This study describes how convergence points in research practices within the fields of art and science can mitigate gender stereotypes associated with the STEM field. A case study of four women working at the intersection of art and science revealed common aspects of their practices: an appreciation of the natural world, a sense of aesthetics, a drawing practice and a reliance on meaningful research questions, suggesting that these can act as bridges between both fields of study. Keywords: Arts; Education; Art-science; STEM; STEAM; Leaky pipeline; Gender; Motivation; Stereotype threat; Self-efficacy; Transdisciplinarity; Nature; Drawing; Aesthetics. Résumé : Comment l’interrelation des réservoirs de connaissance peut-elle contribuer à motiver les jeunes femmes qui se désintéressent de la science ? Cette étude relate comment les points de convergence des diverses pratiques de recherche dans le domaine des arts et de la science peuvent atténuer les stéréotypes de genre associés à la filière STIM. L’étude du cas de quatre femmes œuvrant au point de convergence de l’art et de la science a mis en évidence les aspects communs de leurs pratiques : l’appréciation du monde naturel, un sens de l’esthétique, une pratique du dessin et l’utilisation de questions de recherche pertinentes, ce qui laisse supposer certains ponts entre ces deux domaines d’étude. Mots-clés : arts, éducation, science et art, STIM, STIAM, tuyau percé, genre, motivation, menace du stéréotype, auto-efficacité, transdisciplinarité, nature, dessin, esthétique.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".