Cost–benefit analysis and ‘next best’ methods to evaluate the efficiency of social policies: As in pitching horseshoes, closeness matters
Bibliographic record
Abstract
Abstract Many policymakers are unwilling, or think that it is infeasible, to perform comprehensive cost–benefit analysis (CBA) of programmes in social policy arenas. What principles actually underlie CBA? An understanding is necessary to assess whether other evaluation methods are close enough to CBA to provide useful information on social efficiency. This paper explains five underlying CBA principles and the challenges in applying them to social policy arenas. It assesses a number of ‘less‐than comprehensive’ versions of CBA and analyses their ‘closeness’ to comprehensive CBA and, thus, their value as assessments of changes in social efficiency. We show some types of analysis are not close enough and explain why. We provide a taxonomy of these methods in terms of their comprehensiveness with respect to both social costs and benefits. We also argue that an analysis should provide a clear normative basis for its geographic scope in order to claim it assesses economic efficiency.
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 | 0.056 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".