Regimes and cultures of social security: Comparing institutional models through nonlinear PCA
Bibliographic record
Abstract
This article reassesses the link between the structural and cultural aspects of social security. Do Esping-Andersen’s ‘Three Worlds’ exist empirically if one considers a comprehensive set of formal institutions simultaneously? And if so, do such regimes coincide with coherent differences in people’s value orientations in this field, or informal cultures? In order to answer these questions, nonlinear principal components analysis was applied to a group of countries at the core of the original Esping-Andersen typology. Nonlinear PCA seems to be a promising tool for comparative research because the technique is able to handle discrete data and nonlinear relationships, and the number of variables can exceed the number of countries. The outcomes of the analyses suggest that the ‘Three Worlds’ of formal social security had a firm empirical basis in the 1990s, and that the typology remains largely valid today, albeit with some qualifications. Furthermore, three different informal ‘cultures of social security’ emerged, with country clusters quite similar to those of the structural regime typology.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".