Human Resource Development and Structural Change in Canada – A Provincial or a Federal Approach?
Bibliographic record
Abstract
Canada is the second largest country in the world, with significant geographical distances between population centers. The country’s constitution evolved in response to these distances and gave considerable political autonomy to the Canada’s 13 provinces. Owing to these historical and geographical circumstances it is incorrect to talk about a single labor market, as Canada has a large number of regional labor markets. The restructuring of these labor markets over the past 15 years has increased the numbers engaged in non-standard forms of employment and those working in knowledge-based activities. However, the responsiveness of the country’s education and training system to these changes is open to debate. For example, on the one hand government expenditure on education is very high, amounting to nearly 7% of the country’s GDP, enabling it to have some of the highest post-secondary education participation rates in the world. On the other hand, the current system is criticized for not offering a coherent vocational training route for young people once they have completed their compulsory schooling. Numerous studies have also found that employers experience difficulties in recruiting employees with the appropriate skills and aptitudes necessary for working in a knowledge society. All of these issue are debated further in this chapter, especially in relation into how specific features of Canadian society have shaped the skill trajectories being followed by provinces in Canada. Moreover, the chapter identifies what role a modern state should play in supporting skill development in a post-industrial economy and analyses the degree to which such conditions are present in Canada. By using such an approach we are able comment on the extent to which the present education and training system is facilitating structural change in Canada or not as the case may be.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".