Choix des domaines d'êtudes dans les universités canadiennes
Bibliographic record
Abstract
This study analyses what factors determine the field that students in Canadian universities choose to study. More specifically, we are interested in the effect that the expected lifetime income after obtaining a degree has on this choice. We build an anticipated income variable that takes into account the probability of a student being able to find a job within his/her field of study, in every domain. Using data from the National Graduates Survey (classes of 1986, 1990 and 1995), we evaluate the above-mentioned probability and matching incomes with the help of data available to students on the classes that came before them. Then, with a multinomial logit (mixed) model, we estimate the parameters that determine an individual's choice of discipline by examining seven different fields at the undergraduate level. Our results show that the anticipated income variable is a determining factor in the choice of discipline. However, there is a significant difference between the impact that this variable has on each sex. Women are generally less affected by income variations than are men. We also find that substantial income variations would be necessary to attract some students (i.e. women) in fields of study that they are less likely to choose. Our results show also that there is a strong correlation between the parents' level of education and the children's choices, but this correlation is a function of the sex of both the parent and the child. Finally, we conclude that the choice of the field of study is weakly related to the acquisition of a student loan. A complete version of this report is available, only in French, on the Web site of Industry Canada. Cette étude comporte une analyse des déterminants du domaine d'études choisi par les étudiants des universités canadiennes. Nous nous intéressons plus particulièrement à l'effet du revenu à vie espéré après la remise du diplôme sur ce choix. Nous construisons une variable de revenus anticipés en fonction de la probabilité qu'un étudiant puisse trouver un emploi qui correspond à ses études dans chaque domaine d'études. En utilisant des données de l'Enquête nationale auprès des diplômès (promotions de 1986, de 1990 et de 1995), nous évaluons la probabilité de trouver un emploi dans son domaine d'études et les revenus correspondants en considérant les données disponibles aux étudiants sur les promotions qui les ont précédés. Puis à l'aide d'un modèle logit multinomial (mixte), nous estimons les paramètres qui déterminent le choix individuel d'un domaine d'études en considérant sept domaines d'études de premier cycle. Nos résultats montrent que la variable de revenus anticipés est déterminante dans les décisions des étudiants. Toutefois, il y a des différences significatives dans l'impact de cette variable par sexe. Les femmes sont, en général, moins sensibles aux variations de revenus que les hommes. De plus, nous trouvons que des variations de revenus substantielles seraient nécessaires pour attirer certains étudiants (les femmes par exemple) vers les domaines d'études qu'ils sont moins susceptibles de sélectionner. Nos résultats révèlent également un rapport important entre le niveau de scolarité des parents et les choix de leurs enfants, mais que ce rapport est fonction du sexe du parent et de l'enfant. Enfin, nous concluons que le choix du domaine d'études est faiblement lié à l'obtention d'un prêt étudiant. Une version complète de ce rapport est disponible sur le site Web de Industrie Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".