The State of Training: Learning, Institutional Innovation, and Local Boards for Training and Adjustment in Ontario, Canada
Bibliographic record
Abstract
This paper critiques the learning-region literature on two related points. The first is that the learning-region analysis of labour markets is theoretically underdeveloped, because it underestimates the difficulty of overcoming systematic skill mismatches, underinvestment, and free-rider practices which characterize unregulated labour markets. Second and relatedly, because it does not link the problematic nature of labour-market governance to the conflicts and contradictions of state policy, the learning-region literature effectively ‘depoliticizes’ policymaking. The paper draws on a case study of the development of local boards for training and adjustment in Ontario, Canada, and develops an alternative framework utilizing a critical governance perspective which stresses how knowledge and learning must be seen as part of state accumulation and hegemonic strategies. Such strategies are contingent on the representation of stakeholders, in particular business, and current attempts to develop decentralized associational networks are often part of what Jessop terms metagovernance. In the case of Canada, decentralization from the federal to provincial scales is viewed as crisis and cost driven and in many ways antithetical to stakeholder governance. Thus in Ontario, the development of a stakeholder-based form of labour-market governance has been marginalized by shifts in state-accumulation strategies and the inability and disinterest of business in representing itself in such stakeholder institutions. Furthermore, the local boards' generation of knowledge based on inclusionary networks and information is at odds with a state and business emphasis on knowledge derived from exclusive networks and geared to short-term profit maximization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".