Bossing or Protecting? The Integration of Social Regulation into the Welfare State
Bibliographic record
Abstract
This article is an empirical analysis of how social regulation is integrated into the welfare state. I compare health, migration, and unemployment policy reforms in Australia, Austria, Canada, Belgium, France, Germany, Italy, the Netherlands, New Zealand, Sweden, Switzerland, the UK, and the United States from 1980 to 2014. Results show that the timing of reform events is similar among countries for health and unemployment policy but differs among countries for migration policy. For migration and unemployment policy, the integration of regulation and welfare is more likely to entail conditionality compared to health policy. In other words, in these two policy fields, it is more common that claimants receive financial support upon compliance with social regulations. Liberal or Continental European welfare regimes are especially inclined to integration. I conclude that integrating regulation and welfare entails a double goal: “bossing” citizens by making them take up available jobs while expelling migrants and refugees for minor offenses; and protecting citizens from risks, such as noncommunicable diseases.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".