Varieties of Social Policy by Other Means: Lessons for Comparative Welfare State Research
Bibliographic record
Abstract
Scholars can take a broader look at social policy and understand that traditional public welfare state programs are only one of the many potential sources of social protection and regulation. The contributions of this special issue invite social policy scholars to explore policy instruments that provide “social policy by other means” across a wide array of areas, including agriculture, energy, immigration, taxation, and legal regulation of private benefits and services. The article provides a concise overview of some of the key theoretical and empirical implications of social policy by other means for comparative welfare state research. In order to do this, it is divided into two main sections, which respectively discuss the nature and boundaries of social policy and the varieties of social policy by other means. This is followed by a short conclusion, which summarizes the key lessons of this special issue for comparative welfare state research.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".