Accountability and Funding as Impediments to Social Policy Innovation: Lessons from the Labour Market Agreements for Persons with Disabilities
Bibliographic record
Abstract
Social policy innovation in Canada remains stunted despite recent attempts at social policy renewal via intergovernmental agreements. The fusion of accountability and policy learning is typically blamed, yet this ignores other potential factors. This article examines the Labour Market Agreements for Persons with Disabilities to highlight impediments to social program expansion and reform within governments as well as between governments, and how the design of recent agreements serves to reinforce those impediments. We find that the linkage of accountability and policy learning means that learning gets caught up in long-standing federal-provincial disputes over jurisdiction, and leads to a perverse form of learning. We also find significant barriers to innovation in the nature of federal government funding, which provides neither incentives for “have provinces” to expand their programming nor sufficient funds for “have not” provinces to successfully transform their programs.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".