Revisiting the 'Dual Welfare State': Sickness, Injury & Unemployment Programs in Two 'Liberal' Regimes
Bibliographic record
Abstract
In the 1990s, feminist scholars such as Sainsbury (1993) argued that some countries developed systems of social provision that focused either on social insurance or on social assistance programs, with varying outcomes for men and women. This paper investigates aspects of what Sainsbury called the ‘dual welfare state’, using Canada and New Zealand as case studies. Although both have been labelled as ‘liberal’ or ‘residual’ welfare regimes, the paper focuses on differences in program design for sickness, injury and unemployment that contribute to gendered outcomes. The paper finds that the Canadian programs in these three areas are delivered mainly as social insurance while sickness and employment programs in New Zealand are based on social assistance. Canadian programs exclude many women or pay them less than men, as benefits are based on labour market participation and employment earnings. However, Canadian programs also use the individual as the unit of analysis, providing higher levels of benefits and greater autonomy for partnered women working full-time than do similar programs in New Zealand. This analysis shows that social programs continue to be underpinned by cultural ideas about family and gendered work, about who deserves state assistance, and what role the state should play in promoting health and wellbeing. Social insurance and social assistance not only lead to gendered outcomes but they also generate different consequences for women in varying circumstances, even within similar types of welfare regimes.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".