Could European governance ideas improve federal-provincial relations in Canada?
Bibliographic record
Abstract
Over the past seventeen years Canada has decentralized many social programmes, moving responsibility from the federal government to 13 provinces and territories through bilateral federal-provincial agreements. In contrast, the European Union (EU) has moved in the opposite direction, building pan-European approaches and establishing new processes to facilitate multilateral collaboration among the 28 EU member states. This has been done through a new governance approach called the Open Method of Coordination (OMC). Using a detailed case study - employment policy - this paper explores whether Canada could learn from OMC governance ideas to re-build a pan-Canadian dimension to employment policy and improve the performance of its intergovernmental relations system. Concrete lessons for Canada to improve decentralized governance are suggested: consolidating the different bilateral agreements; using benchmarking instead of controls in fiscal transfers; undertaking research, analysis, and comparisons in order to facilitate mutual learning; revitalizing intergovernmental structures in light of devolution; and engaging social partners, civil society and other stakeholders. Post-devolution Canada is not doing badly in managing employment policy, but could do better. Looking to the EU for ideas on new ways to collaborate provides a chance for setting a forward looking agenda that could ultimately result not only in better labour market outcomes, but also improvements to one small part of Canada's often fractious federation.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".