Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it