Institutional moral hazard in the multi-tiered regulation of unemployment in Canada: Background paper
Bibliographic record
Abstract
This paper has been written in preparation of a research project funded by the European Commission (on the Feasibility and Added Value of a European Unemployment Benefit Scheme, contract VC/2015/0006). This paper adds information and detailed analysis to the following deliverable of that research project: Institutional Moral Hazard in the Multi-tiered Regulation of Unemployment and Social Assistance Benefits and Activation - A summary of eight country case studies; but it was not a deliverable. We use the concept ‘institutional moral hazard’ to analyse intergovernmental relations within multi-tiered welfare states, specifically the domain of in unemployment-related benefits and related activation policies (the ‘regulation of unemployment’). This paper is one of eight separate case studies, it focuses on Canada. Responsibilities in the Canadian regulation of unemployment are divided between the federal government and the provinces. The federal government is responsible for unemployment insurance benefits, the provinces for social assistance and activation of all caseloads. Provincial activation responsibilities are loosely regulated by bilateral agreements with the federal government. Even though this system generates institutional moral hazard, it does not seem to be a major federal concern. The regulation of unemployment is characterised by a high level of provincial autonomy.
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".