Bringing in the third pillar: protective legislation and the welfare state
Bibliographic record
Abstract
Welfare state research over the past five decades has largely focused on two broad social policy pillars: (1) arrays of income programmes, such as social assistance, unemployment insurance, family allowances and pensions, and (2) a range of social services, including childcare, healthcare, social housing and education. However, an important cluster of policy measures, constituting a third policy pillar, has been largely neglected in the cross-national welfare state research to date, protective legislation. It comprises the networks of protective and regulatory laws and constitutional rights emplaced to secure and promote our welfare. Central to all social policy areas, these measures, including rent control policies, workplace health and safety laws, legislation preventing discrimination against people with disabilities and child corporal punishment bans, are distinct from laws that determine access to income programmes and social services or set out the terms of their provision. Rather, they constitute a standalone third category of policy measures, are key components of all welfare states and, depending upon how they are actualised, can be just as central to our well-being and the promotion of our welfare. They can also have compounding or synergistic interactions with policies in the other two policy pillars.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".