Bibliographic record
Abstract
This paper examines the determinants of the choice of field of study by university students. Specifically, we are interested in the impact expected post-graduation lifetime earnings have on this decision. We construct a variable for expected earnings as a function of the probability that students will be able to find employment corresponding to their field of study for each major. Using data from the Canadian National Graduate Survey (cohorts 1986, 1990 and 1995), we assess the probability that students of each cohort will find work in their discipline, and the corresponding earnings, using the data available for the preceding cohort. Subsequently, we use a mixed multinomial logit model to estimate the parameters of individuals’ choices of field of study for seven broadly defined majors. Our results reveal that expected earnings are determinant in the students’ choices, but that there are significant differences between genders in the impact of this variable. In general, women are less sensitive than men to income variations. We also conclude that substantial variations in income would be required to overcome the educational segregation evinced by the preponderance of a gender in certain fields of study. Finally, we conclude that parents’ level of education has a significant influence on their children’s choices, but that this choice is a function of both the parent’s and the child’s sex.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".