Feminisation of poverty in 12 welfare states: Strengthening cross-regime variations?
Bibliographic record
Abstract
The feminisation of poverty is said to have become a common feature in the majority of advanced welfare states, but it is equally true that there has been significant variation in the feminisation of poverty from one country to another. While the concept of the feminisation of poverty remains controversial, there have been very few attempts to reveal a detailed picture from a comparative perspective. Considering this background, this study aims to illustrate the feminisation of poverty in 12 welfare states (Liberal - Australia, Canada, UK, US; Conservative - Austria, France, Germany, Italy; Nordic - Denmark, Finland, Norway, Sweden) between the 1980s and the 2000s and to analyse whether or not there has been any convergence or divergence between these welfare states. This study will evaluate the scope and depth of the feminisation of poverty by conducting analyses not only in terms of different sex, but in terms of different population groups. Further, the changing role of welfare states will be assessed via an analysis of the antipoverty role of public transfers in each country. The Luxemburg Income Study dataset will be used for empirical analysis. This paper will argue that while the feminisation of poverty has been slowed down and even reversed in certain cases, cross-national differences have been increasingly visible. The results of this study also show that the welfare regime framework can prove to be a useful tool for understanding the similarities and the differences in the feminisation of poverty across different Western welfare state 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".