L’identité scientifique chez les étudiantes en STIM: une recension des écrits
Bibliographic record
Abstract
Research on the underrepresentation of women in science, technology, engineering, and mathematics (STEM) increasingly adopts a perspective based on science identity. This approach allows for consideration of both individual and social factors that shape female students’ educational paths in STEM. This literature review aims to provide an overview of key writings on the concept of science identity (Carlone & Johnson, 2007), primarily from anglophone research. We explore the definition of science identity, its components, and its variations in specific STEM disciplines, such as physics (Hazari & al., 2010) and engineering (Capobianco, 2006 ; Godwin & al., 2016). Despite the numerous existing studies, the concept of science identity has been slow to make its way into francophone research in science education, which could benefit from its theoretical contributions. We thus describe how science identity could serve as a relevant theoretical tool for studying the school experiences of female students in STEM fields in Quebec, particularly at the college level, which represents a pivotal moment in their orientation in higher education.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".