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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".