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 etude comporte une analyse des determinants du domaine d'etudes choisi par les etudiants des universites canadiennes. Nous nous interessons plus particulierement a l'effet du revenu a vie espere apres la remise du diplome sur ce choix. Nous construisons une variable de revenus anticipes en fonction de la probabilite qu'un etudiant puisse trouver un emploi qui correspond a ses etudes dans chaque domaine d'etudes. En utilisant des donnees de l'Enquete nationale aupres des diplomes (promotions de 1986, de 1990 et de 1995), nous evaluons la probabilite de trouver un emploi dans son domaine d'etudes et les revenus correspondants en considerant les donnees disponibles aux etudiants sur les promotions qui les ont precedes. Puis a l'aide d'un modele logit multinomial (mixte), nous estimons les parametres qui determinent le choix individuel d'un domaine d'etudes en considerant sept domaines d'etudes de premier cycle. Nos resultats montrent que la variable de revenus anticipes est determinante dans les decisions des etudiants. Toutefois, il y a des differences significatives dans l'impact de cette variable par sexe. Les femmes sont, en general, moins sensibles aux variations de revenus que les hommes. De plus, nous trouvons que des variations de revenus substantielles seraient necessaires pour attirer certains etudiants (les femmes par exemple) vers les domaines d'etudes qu'ils sont moins susceptibles de selectionner. Nos resultats revelent egalement un rapport important entre le niveau de scolarite 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'etudes est faiblement lie a l'obtention d'un pret etudiant. Une version complete de ce rapport est disponible sur le site Web de Industrie Canada.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".