Le Choix des Filières d'Études au Québec : Situation Actuelle et Revue de la Littérature
Bibliographic record
Abstract
In this paper, we have examined the over-representation of women in the university sector as a whole and their under-representation in the pure and applied sciences. Paving the way for concrete responses, our overview of the economic literature examined the determinants of university students' choice of fields of study. It was found that students who faced multiple choices of study disciplines discriminated between programs based on anticipated income differentials, the likelihood of finding employment, the atrophy of knowledge in science, and the value of some degrees that facilitate the transition to higher education. Although these variables significantly explain the choice of students' programs of study, a significant residual portion of the gender gap in choice remains unexplained. We suggest that a number of idiosyncratic preferences of women and men could explain this residual portion, but that data on student preferences in Quebec were not available in the current survey. To fill this gap, laboratory experiments currently underway at CIRANO will soon shed substantial light on risk aversion and the distinctive degree of cooperation between men and women. We believe that these two factors play an important role in students' decision-making processes and their measurement, in addition to enriching current educational knowledge, would pave the way for the development of policy interventions. Dans le présent document, nous nous sommes interrogés sur la sur-représentation des femmes dans l’ensemble du secteur universitaire et leur sous-représentation dans le domaine des sciences pures et des sciences appliquées. Préparant la voie à des réponses concrètes, notre survol de la littérature économique s’est penché sur les déterminants des choix de filières des étudiants universitaires. Il a été possible de constater que les étudiants qui faisaient face à plusieurs choix de disciplines d’études discriminaient entre les programmes selon les différentiels de revenus anticipés, les probabilités de trouver un emploi, l’atrophie des connaissances dans les sciences ainsi que la valeur que procurent certains diplômes qui facilitent le passage aux études supérieures. Bien que ces variables expliquent significativement le choix des programmes d’études des étudiants, une portion résiduelle importante de l’écart des choix entre les hommes et les femmes demeure toujours inexpliquée. Nous avançons qu’un certain nombre de préférences idiosyncratiques des femmes et des hommes pourraient expliquer cette portion résiduelle mais que des données sur les préférences des étudiantes et étudiants au Québec n’étaient pas disponibles dans les enquêtes de sondage actuelles. Pour combler ce vide, des expériences en laboratoire présentement en cours au CIRANO permettront sous peu de jeter un éclairage substantiel sur l’aversion au risque et le degré de coopération distinctif des hommes et des femmes. Il est de notre avis que ces deux facteurs jouent beaucoup dans le processus de décision des étudiants et leur mesure, en plus d’enrichir les connaissances actuelles en matière d’éducation, préparerait la voie à l’élaboration de politiques d’interventions.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".