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’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".