Bibliographic record
Abstract
Abstract In this final piece to the symposium for a special issue ofPacific Economic Reviewon the theories and applications of second‐best and third‐best theories, Richard Lipsey and Yew‐Kwang Ng provide their final comments to the debate. Several issues of agreement and disagreement are discussed. Most importantly, while both agree on the formal correctness of both the second‐best and third‐best theories, Lipsey believes the main proposition of third‐best theory (following the first‐best rules under Informational Poverty) is applicable only to a situation (status quo) where the first‐best rule (such as taxing a pollution at the marginal damage of $N) is already being followed; Ng regards it as applicable whether or not the first‐best rule is currently being followed. This also partly explains their difference on the practical policy relevance and the importance of that theory.
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 | 0.027 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".