Tertiary Education: Developing Skills for Innovation and Long-Term Growth in Canada
Bibliographic record
Abstract
The tertiary education system in Canada performs well in fostering a skilled workforce with generally good labour-market outcomes and is internationally recognised for its research contributions. Tertiary educational attainment is high, but participation rates will need to continue expanding to maintain the supply of highly skilled labour as the population ages and the needs of the knowledge-based economy rapidly evolve. This should be achieved by encouraging access to higher education for disadvantaged socio-economic groups, while enhancing the flexibility of the system to allow students with diverse needs to move between institutions more easily to meet their learning objectives. Immigration is another important source of skills that could be better utilised. The development of skills for innovation can be improved by increasing the integration of technical, business and communications skills training with practical industry experience within tertiary education programmes. In an environment of government spending restraint, the quality of tertiary education could be strengthened by increasing the distinction between institutions that target research and those that emphasise teaching and re-evaluating tuition policies in provinces where public finances are stretched. This Working Paper relates to the 2012 OECD Economic Review of Canada (www.oecd.org/eco/surveys/Canada).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".