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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".