Skilling Up for the Knowledge Economy Assessing the Returns to STEM Skills and Bilingualism Using the 2018 National Graduates Survey
Bibliographic record
Abstract
Although the increasing importance of "soft skills" would suggest stronger labour market returns for BHASE* fields, our findings show that STEM jobs still pay more. As well, being bilingual in Canada’s official languages is associated with higher earnings in BHASE fields—and that this earnings advantage holds for both non-technical and technical BHASE jobs. Considering the clear advantage that STEM-related skills can have on improving career prospects for early graduates, our report supports the opinions of many education experts pushing for STEM programming to be introduced earlier in education cycle. Similarly, the employment advantage that non-STEM graduates have by being bilingual in Canada’s two official languages (particularly if they wish to work in the public sector) could explain why second language immersion programs have grown increasingly popular. Our report’s findings have far-reaching implications for policy makers interested in future-proofing Canada’s labour force, and it discusses the possibility of retooling existing education system—for both youths and adult learners—to ensure the best returns to public 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".