LES LIENS ENTRE LES VALEURS, LES INTÉRÊTS, LES APTITUDES ET L’ESTIME DU SOI DES JEUNES FILLES ET LEURS CHOIX D’ÉTUDES ET DE CARRIÈRE
Bibliographic record
Abstract
La sous‐représentation des femmes dans les domaines d’études et d’emploi non traditionnels est une tendance continue, particulièrement chez les femmes appartenant aux groupes culturels minoritaires. Des études indiquent que les déterminants liés au problème du développement de carrière des filles constituent un ensemble complexe d’éléments interdépendants comprenant des influences sociales, familiales et scolaires et des caractéristiques individuelles. Une enquête réalisée auprès de jeunes filles vivant en contexte francophone minoritaire permet de mieux éclairer cette problématique. Mots‐clés : choix de carrière, nouvelle économie, filles/femmes, système de valeurs, langue minoritaire The under‐representation of women in non‐traditional jobs and fields of study is a continuing trend, particularly among women in cultural minorities. Research indicates that the determining factors in women’s career development are a complex group of interdependent elements including social, familial, and academic influences and individual characteristics. This study of young girls in a minority Francophone environment helps to clarify the issue. Keywords: career choice, new economy, girls/women, value systems, minority language
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".