Convergence or Resilience? A Hierarchical Cluster Analysis of the Welfare Regimes in Advanced Countries
Bibliographic record
Abstract
Following the seminal work of Esping-Andersen, many studies have identified a variety of welfare regimes in Western Europe and North America. This study examines a set of quantitative social indicators, using hierarchical cluster analysis, in order to identify such regimes, which display specific arrangements between markets, the state and families in the production and distribution of the resources required for the well-being of people. Indeed, these empirical analyses reveal the existence of the three regimes originally identified by Esping-Andersen - social-democratic, liberal, and conservative - to which one must add, as many authors had pointed out, a fourth, distinct from the latter, the Latin regime. These results pertain whether one turns to data from the 1980s or the 1990s. The data also reveal strong and durable relations of presumably mutual causality between the configuration of social programmes in the various societies under analysis, the social situations which largely result from these social programmes and, lastly, the level of civic participation which leads (or not) people to collective mobilization, which in turn shapes social programmes. The authors' comparative analysis allows them to identify Canada's place in the worlds of welfare capitalism.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".