Bibliographic record
Abstract
After facing disappearance during the thirties, under the well known attack by Lionel Robbins and other economists, theoretical welfare economics seems to be more and more in use in spite of still shaky foundations. The concept of surplus, the measurement of preferences and the economic welfare function are apparently among the most promising approaches. Welfarists like Tinbergen and Bergson are not so far now from known anti-welfarists like Robbins and Myrdal. However, the issue of the debate between Pigou and Robbins has to be reconsidered before we can use welfare theory. New methodological arguments are presented that try to separate the shaft from the weed in the thesis of Pigou and Robbins. Some errors are found that have remained alive up to now. After a new light is shed on the controversy, it is fairly easy reconcile Pigou, Robbins, Bergson, Myrdal and Tinbergen. It is fortunate that we can reach such a consensus because the theory of economic policy badly needs renewed foundations to be able to cope with changing values, new patterns of distribution and other long range problems. A fresh start with the questions raised by welfare economics should ease the way towards these goals.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".