Bibliographic record
Abstract
I analyze a market in which a price-taking buyer buys a variable-quality good from a population of sellers, contrasting the case where quality is a seller's private information to that where it is public information. Average quality traded under private information can be either higher (quality oversupply) or lower (quality undersupply) than under public information, depending on sellers' preferences. We are likely to see quality undersupply if (i) sellers' preferences exhibit substitutability between the variable-quality good and the numéraire good, and/or (ii) sellers view the numéraire good as a luxury good relative to the variable-quality good. Reverse arguments hold for quality oversupply. J'analyse un marché dans lequel un acheteur achète un bien de qualité variable d'une population de vendeurs. Je compare deux cas, celui où la qualité est connue des vendeurs seulement (information privée) et celui où la qualité est connue publiquement (information publique). La qualité moyenne vendue sur le marché avec information privée peut être supérieure ou inférieure à la qualité moyenne vendue sur le marché avec information publique, selon les préférences des vendeurs. Elle aura tendance à être supérieure si (i) les préférences des vendeurs manifestent une substituabilité entre le bien à qualité variable et le bien numéraire, et/ou (ii) les vendeurs considèrent le bien numéraire comme un bien de luxe par rapport au bien à qualité variable.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".