Towards Electronic Marketplaces: A Progress Report
Bibliographic record
Abstract
Market design is becoming a very important research topic in the context of the electronic economy. Two factors explain this trend: (1) the creation of new markets to facilitate deregulation (telecommunication frequencies, electric power, etc.) and (2) the emergence of strategic analysis and of experimental economics. E-commerce tools will contribute to the emergence of more structured marketplaces. Private service providers will begin offering price-discovery and demand-and-supply equilibrating mechanisms. Market design issues are therefore becoming important for private e-commerce service providers. The TEM (Towards Electronic Marketplaces) project investigates issues related to the design of virtual marketplaces, whether these marketplaces are centralised or decentralised. In this paper, we present the global objectives of the TEM project, along with its specific goals, and current results. In particular, we focus on the challenges for electronic markets and on the approaches used to meet these challenges.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".