From Retrenchment to Selective Social Policy Expansion: The Politics of Federal Cash Benefits in Canada
Bibliographic record
Abstract
Abstract While much has been written about the politics of retrenchment, in a number of advanced industrial societies social policy expansion does occur today, which raises issues about how to study it in a post-retrenchment era. The present article explores the new politics of social policy expansion in Canada. Drawing on the work of Paul Pierson, we use an integrated framework that highlights the interaction of five factors: the availability of fiscal resources; the emergence of new social risks; the intensity and nature of partisan competition; the policy preferences of the main political parties; and the role of political institutions, especially federalism. Empirically, the article studies the politics of federal social policy expansion during the Harper (2006–2015) and Justin Trudeau (2015–) years, with a focus on three policy areas: child benefits (Universal Child Care Benefit and Canada Child Benefit), pensions (Old Age Security and Canada/Quebec Pension Plan) and Employment Insurance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".