Bibliographic record
Abstract
One view regarding auction duration suggests that longer auctions would result in more bidders and more bids, which in turn would result in higher prices. An opposing view is that shorter auctions might appeal to impatient bidders, or alternatively, that shorter duration might lead to more competitive dynamics. To examine these competing notions, we conduct pairwise comparisons of simultaneous auctions identical in all but duration. The auctions are conducted on two different platforms—eBay and a local auction site. We find that in eBay auctions, longer duration increases the number of bidders and bids, and consequently increases final prices by about 11%. On the local auction website, with far fewer auctions and a more steady set of participants, the effect is reversed, and shorter auctions generate higher prices by about 20%. Both sets of effects are robust and significant. We look at bidding activity on both sites to try to get at the root of that reversal. We find that in eBay auctions, the higher price in the longer-duration auction is accompanied by a higher number of participating bidders and a higher number of bids placed in the auction. In the local site, we find that the auction duration does not significantly affect the number of participating bidders or the number of bids placed in an auction. However, the magnitude of jump bids is negatively and significantly correlated with duration. These jump bids are in turn shown to impact final prices.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".