Championing and shaming in a credence good market: Which one to use?
Bibliographic record
Abstract
Abstract We analyze the performance of the championing and shaming inquiries by a Nongovernmental Organization in a signaling game played by a monopoly that sells a credence good to an uninformed consumer. Championing (shaming) means certifying (uncovering) a firm that sells a high (low) quality product. An inquiry alters the whole information structure of the signaling game. It provides redundant hard information in a separating equilibrium but it lowers the set of separating prices. We show that a high‐quality producer and the consumers welcome this inquiry in a pooling equilibrium as it enhances their expected payoffs. They prefer a championing over a shaming inquiry when the likelihood of a high‐quality producer is low. A championing inquiry may lower the consumer's expected payoff if it is run before the monopoly sets its price since the consumer may prefer paying a low pooling price for a credence good rather than a high price for a certified high‐quality good.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".