Bibliographic record
Abstract
Reverse pricing is a market mechanism under which a consumer's bid for a product leads to a sale if the bid exceeds a hidden acceptance threshold the seller has set in advance. The seller faces two key decisions in designing such a mechanism. First, he must decide where in the process to collect the revenue—that is, whether to commit to a minimum markup above cost (and thus define the bid-acceptance threshold given cost) and whether to set a fee for the consumer's right to bid. Second, the seller must decide whether to facilitate or hinder consumer learning about the current bid-acceptance threshold. We analyze these decisions for a profit-maximizing small intermediary retailer selling to consumers who can also purchase the product in an outside posted-price market. The optimal revenue model is to charge a fee for the right to bid and then accept all bids above cost, rather than to set a positive minimum markup above cost. Avoiding minimum markups in favor of a bidding fee is more profitable because of increased efficiency arising from more entry by consumers and higher bids by the entrants. When consumers learn about the bid-acceptance threshold before they enter the market, efficiency increases further, and generating revenue through a bidding fee can compensate the seller for his loss of information rent when the competition from the outside posted-price firm is relatively weak.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".