"There are enough employees in the workforce: training them is key"
Bibliographic record
Abstract
When it comes to the current debate on skills and employment in Canada, however, it may be the "unknown knowns" that are most important. We have oceans of reports and statistics but if their findings are not synthesized, research can't guide policy and won't tell new graduates much about the labour market of today, or how to prepare for the future. So what happened when 16 teams of researchers working across the country took a look at the existing research on skills and labour markets? Here is some of what they discovered: 1. Canada is unlikely to face a generalized shortage of skilled labour now or in the coming decades, despite an aging population and the changing skill requirements of many occupations. Our labour force continues to expand due in part to longer work lives, while important groups of workers (youth, aboriginal, persons with disabilities, skilled immigrants) are significantly "under-utilized" in the labour market. As in the past, future skills gaps are likely to be cyclical and focused on particular industries and regions.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".