Bibliographic record
Abstract
This book has examined the context, role, design and impacts of income-related housing allowances in a variety of countries. Chapters Two to Ten looked at nine advanced welfare states, while Chapter Eleven explored the experience of the Czech Republic and made comparisons with several other transition economies. This final chapter draws upon the preceding chapters to reflect upon the role of housing allowances in the advanced welfare states. The first section describes the broad welfare regime and housing market context of the ten main countries covered in the book. The second section compares important features of income-related assistance with housing expenditures across these countries. The third section examines key reform pressures and debates about housing allowances and the fourth section focuses on ‘housing vouchers’ as a possible future for income-related assistance with housing expenditures. The final section presents some conclusions. Although housing allowances have become an important policy instrument in many of the advanced welfare states, they are embedded within different national contexts. Table 12.1 summarises some key features of the social protection systems in each country covered by this book. Nine of the 10 countries were included in Esping-Andersen’s typology of welfare regimes (the exception being the Czech Republic). Australia, New Zealand, Canada, the US and Great Britain were described by him as liberal welfare regimes, characterised by a low level of benefits, reliance on means testing and a relative emphasis on private social provision.1 France and Germany were classified as conservative welfare states, characterised by heavy reliance on status-maintaining social insurance schemes with relatively generous, earnings-related benefit levels and an emphasis on the ‘male breadwinner family model’.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".