Cyberspace Auctions and Pricing Issues: A Review of Empirical Findings
Bibliographic record
Abstract
This article surveys empirical findings from recent studies of Internet auctions and summarizes the theoretical insights gained from these findings. The main questions answered in this article are: What are the price formation mechanisms used in Internet auctions? What are the main causes of informational asymmetry in these online environment, and how can the extent of these asymmetries be quantified? Does observed bidder behavior conform to the theoretical predictions from game theory? How do the findings regarding bidder behavior on Internet Recently, we have seen an explosion in the number and dollar volume of transactions between consumers and businesses. According to the Census Bureau of the Department of Commerce, the dollar volume of retail e-commerce in the 4th quarter of 2001 was $10 billion dollars, which has nearly doubled from $5.27 billion dollars in the 4th quarter of 1999. E-commerce accounted
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".