Generality Versus Context Specificity: First, Second and Third Best in Theory and Policy
Bibliographic record
Abstract
Abstract Second‐best theory established that a policy's effect on community welfare (or any other objective function) varies with its specific context. In contrast, Ng argues that fulfilling first‐best conditions piecemeal is optimal whenever the policy‐maker's information is insufficient to determine the direction of the change in the variable under consideration that will raise welfare, irrespective of the conditions in that market. It is argued in the present paper: (i) that Ng's own assumptions imply not that first‐best conditions should be established under these circumstances, but that the status quo should be maintained; (ii) that when Ng's key assumption is altered to be empirically relevant, all policy decisions become fully context‐specific; and (iii) that Woo's argument for accepting Ng's conclusions in spite of point (ii) is incorrect. The conclusion discusses valid uses of piecemeal welfare theory in spite of second best.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.037 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".