Revisiting the Labour Market Outcomes Associated with Postsecondary Education: An Analysis of the 2009/2010 Cohort of Canadian University Graduates By Program
Bibliographic record
Abstract
Drawing on data from the 2009/2010 National Graduate Surveys, this doctoral research builds upon past research comparing the outcomes of university graduates of various postsecondary programs across earnings, objective and subjective work-to-education match, and job satisfaction. This research consists of four areas of study. Statistical analyses first compare fields of study using disaggregated categories of liberal arts and STEM (science, technology, mathematics, and engineering) programs, to determine the extent to which the labour market outcomes vary for graduates of university programs that are traditionally aggregated in the wider literature. The next stage of research analyzes the outcomes of postsecondary graduates of traditional versus non-traditional (distance education) programs. Comparisons of outcomes of university graduates who specialized in bilingual versus technical (i.e., science, technology, engineering, or mathematics) pursuits comprise the third area of study. Finally, statistical models include sociodemographic variables to assess whether traditional dimensions of disadvantage have remained salient in the outcomes of university graduates from the most recent wave of the National Graduate Surveys. Statistical analyses are comprised of descriptive statistics, ordinary least squares (OLS) regression, binary logistic regression, and graphical displays of predicted probabilities. This dissertation revisits the debate regarding the viability of human capital, credentialist, and labour market segmentation approaches. The policy implications of the results are also discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".