Prepared for the Oxford Handbook of Political Economy. For helpful comments we thank
Bibliographic record
Abstract
the University of Western Ontario. Winer's research was supported by a Canada-United States Fulbright Scholarship during the 2003/2004 academic year. The hospitality extended to Winer by the Department of Economics and the Center for the Study of Democracy at U.C. Irvine is also gratefully acknowledged. Errors and omissions remain the responsibility of the authors. No one can quarrel with the requirement that the budget plan should be designed to maximize social welfare… Musgrave and Peacock (1958, ix) At first sight it might be argued that anyone who set himself up as a judge and wished to establish standards for the distribution of expenditure or amounts of revenue different from those approved by Parliament, must belong to one of three categories: either he must be mentally the equal of that average intelligence [in Parliament], in which case he could not arrive at a different judgment; or he must be inferior to it, in which case his opinion would be less reliable; or he must be superior to it, which could not be proved. Pantaleoni (1883, 17) Economic journals continue to publish a steady stream of articles dealing with the public sector.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.252 | 0.059 |
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".