What needs to be done in contingent valuation: have Smith and Sach missed the boat?
Bibliographic record
Abstract
It is possible to stretch analogies too far, which is how some readers may interpret this response to Smith and Sach’s latest journey on the good ship ‘willingness-to-pay-database’. They can be dangerous tools to use too, if only because it is difficult to resist the inclination to respond in kind! In their paper, ‘Contingent valuation: what needs to be done?’, Smith and Sach seek to show that contingent valuation (CV) research in health is like a ship without a sail. The solution they arrive at is to suggest ‘more guidelines needed’, although it is not clear if they mean guidelines for reporting of studies or guidelines for the conduct of studies.
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 | 0.057 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.012 | 0.032 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.095 | 0.092 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".