Contemporary Guidance for Stated Preference Studies
Bibliographic record
Abstract
This article proposes contemporary best-practice recommendations for stated preference (SP) studies used to inform decision making, grounded in the accumulated body of peer-reviewed literature. These recommendations consider the use of SP methods to estimate both use and non-use (passive-use) values, and cover the broad SP domain, including contingent valuation and discrete choice experiments. We focus on applications to public goods in the context of the environment and human health but also consider ways in which the proposed recommendations might apply to other common areas of application. The recommendations recognize that SP results may be used and reused (benefit transfers) by governmental agencies and nongovernmental organizations, and that all such applications must be considered. The intended result is a set of guidelines for SP studies that is more comprehensive than that of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, is more germane to contemporary applications, and reflects the two decades of research since that time. We also distinguish between practices for which accumulated research is sufficient to support recommendations and those for which greater uncertainty remains. The goal of this article is to raise the quality of SP studies used to support decision making and promote research that will further enhance the practice of these studies worldwide.
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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 | 0.315 | 0.596 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".