Work Integrated Learning—Does it Provide a Labour Market Advantage? Evidence from the 2018 National Graduates Survey
Bibliographic record
Abstract
Co-ops, internships, and other activities that integrate students’ academic studies within a workplace setting—described here as “work-integrated learning” (WIL)—have become an increasingly popular strategy to bolster the employability of post-secondary graduates. Indeed, in 2020 the federal government invested $200 million into the Student Work Placement Program to support the creation of roughly 20,000 new placement for post-secondary students—and that’s only the most prominent WIL initiative they invested in. Despite the enthusiasm, there is not currently enough reliable, up-to-date labour market data available to support the benefits of WIL. This RIES report addresses this lacunae, using the 2018 National Graduates Survey (NGS), to produce additional, robust evidence on the increased labour market returns for post-secondary graduates who had WIL as part of their curriculum. Fortunately for policymakers, our analyses appear to show tangible benefits to WIL in Canada, as observed through a range of metrics. However, these benefits are not evenly distributed. We discuss these trends and their implications for future research and policy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".