Postsecondary pathways and graduate earnings: does transfer make cents?
Bibliographic record
Abstract
Post-secondary education (PSE) experienced explosive growth and diversification over the past century, affording students a range of increasingly complex pathways that they can travel to acquire a credential. In various jurisdictions, governments have made significant investments to facilitate student uptake of unconventional transfer pathways that involve stops at multiple institutions. But, there has been limited effort to understand the impact of travelling these unconventional pathways on graduate labour market outcomes. Moreover, existing studies across many jurisdictions typically lack access to detailed measures of academic performance, demographics, and other pertinent controls that could explain the relationship between PSE pathway uptake and labour market outcomes. Through this study we model this relationship by drawing on a large custom linkage between student records from the Toronto District School Board (TDSB) and three large administrative datasets housed in Statistics Canada’s Education and Labour Market Longitudinal Linkage Platform (ELMLP). This linkage offers census-level coverage of the population of interest and allows us to longitudinally track students from their Grade 9 year at the Toronto District School Board (TDSB), into and through Ontario PSE, and then as they enter the labour market. Our analyses unearth a series of pathway-based earnings disparities that prove robust to available controls. We elaborate on the implications of these findings for policymakers and draw attention to their relevance for scholars interested in school-to-work transitions in Canada and abroad.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".