Bibliographic record
Abstract
The idea of targeting within universalism has been evoked frequently, usually as a best of both worlds’ strategy. The approach remains difficult to identify, however, because targeting is usually measured as the opposite of universalism. This article proposes to consider targeting and universalism as two distinct dimensions of the welfare state, the opposite of universalism being more usefully understood as residualism, and not as pro-poor targeting. Four welfare state possibilities then emerge, combining a position on the universalism/residualism axis and one on the pro-poor/pro-rich axis: universalism (France, for instance), targeting within universalism (Denmark), targeting within residualism (the United States) and pro-rich residualism (Japan). We show that targeting within universalism entails pro-poor targeting without means testing, a combination that can be achieved with limits on the earnings-relatedness of the pension system and generous transfers to the working age population. Thus understood, targeting within universalism proves to be an effective redistributive strategy, better to redistribute than mere targeting, and less costly than universalism pure and simple.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".