Bibliographic record
Abstract
If agents engage in resale, it changes bidding in the initial auction. Resale offers extra incentives for bidders with lower valuations to win the auction. However, if resale\nmarkets are not frictionless, then use values affect bidding incentives, and stronger bidders still win the initial auction more often than weaker ones. I consider a first price auction followed by a resale market with frictions, and con�rm the above statements.\nWhile intuitive, our results differ from the two bidder case of Hafalir and Krishna (2008): the two bidders win with equal probabilities regardless of their use values. The\nreason is that they face a common (resale) price at the relevant margin, a property that fails with more than two bidders. Numerical simulations show that asymmetry in winning probabilities increases in the number of bidders, and in large markets resale loses its e¤ect on allocations.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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".