Bibliographic record
Abstract
In this paper, we explore the use of the web as an environment for electronic commerce. In particular, we develop a novel mechanism that creates incentives for honesty in electronic marketplaces where human users are represented by buying and selling agents. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neigh-bors from which they can ask advice about sellers. In addi-tion, however, sellers model the reputation of buyers. Rep-utable buyers provide fair ratings of sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyers, in order to build their reputation. We discuss how a marketplace operating with our mechanism leads to better profit both for honest buyers and sellers. With honesty encouraged, our work promotes the ac-ceptance of web-based agent-oriented e-commerce by human users.
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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 | 0.099 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".