BOOK REVIEW Housing Allowances in Comparative Perspective
Bibliographic record
Abstract
In many advanced Northwestern European welfare states, a shift occurred in the second half of the last century from supply-side to demand-side subsidies, the most common form being called housing allowances (HA), though they exist under diVerent names. Nowadays their aim is to ensure that homes of reasonable quality are aVordable to lower-income households (income dependency). In countries such as Germany, the Netherlands, Sweden, the US and Australia, HA have become a key instrument in housing policy, now that supply-side subsidies have been reduced or abolished. Ten years after Peter Kemp’s (1997) international comparison of HA, the many changes warrant a new publication. Both books acknowledge that HA are part of a broader welfare system, and both use welfare system classiWcations to justify the selection of countries to be compared. The 2007 book adopts the welfare regimes of Esping-Andersen (1990) when classifying Wve of the ten countries as a liberal regime (Australia, New Zealand, Canada, US and Great Britain) and two each as conservative (France and Germany) and social democratic regimes (Netherlands and Sweden). In order to include a country in transition to a market economy, the new book contains a chapter on the Czech Republic, though it is not
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".