Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it