Bibliographic record
Abstract
Abstract: In this paper, I estimate a structural model of choice of field of study by community college students. I use data from the Canadian Survey of Graduates for 12,871 individuals who successfully completed their programs in Canadian community colleges (CEGEPs in Quebec) in 1990 and 1995. Over this period, the returns to fields such as health declined relative to other fields such as science and engineering, a fact that provides useful leverage for identifying the impact of earnings on the choice of field of study. Results indicate that the probability of selecting a specific college field of study depends significantly on expected earnings in this field relative to other fields. I also find that women put less weight on earnings than men when choosing a field of study and those students who were employed prior to starting college are more sensitive to earnings variations across fields of study than students with no prior work experience. I would like to thank Thomas Lemieux, David Green, Claude Montmarquette, Dwayne Benjamin, Nour Meddahi and Martin Fournier for many useful comments and suggestions. I also thank participants at the empirical micro seminar at the University of British Columbia, TARGET workshop and Canadian Economics Association 2003 Conference (Ottawa) for their helpful comments. Many thanks also go to Statistics Canada for providing the data used in this study. I gratefully acknowledge financial assistance from SSHRCC INE Grant "Globalization, technological revolutions and education."
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".