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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.023 |
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