Bibliographic record
Abstract
Abstract This paper presents the editors’ introduction for a symposium on Second and Third Best Theory forthcoming in The Pacific Economic Review, 22:2, May 2017. Unusual in such cases, the editors are the major protagonists in the debate. In the symposium Ng maintains that second‐best theory appears to preclude giving theory‐based policy advice because full second‐best optima can never be determined in practical cases. While agreeing about second‐best optima, Lipsey disagrees with Ng's conclusion regarding policy and discusses the development of context‐specific policies not based on the theory of optimal resource allocation. To allow for theory‐based policy, Ng offers his theory of third best. The major disagreement over this theory concerns its proposition: first‐best rules for third‐best worlds under Informational Poverty (not enough is known to determine the desirable direction of change of some the policy variable from the first‐best value). Lipsey argues that, if correct, this rule would upset the main result of second‐best theory that the sign of the change in the objective function may be either positive or negative when first‐best rules are fulfilled piecemeal in second‐best worlds. Woo supports Ng's third‐best theory and derives additional rules, while Boadway surveys the application of second‐best theory in several cases from the literature of public economics.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.241 | 0.149 |
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".