Proceedings of the 11th ACM conference on Electronic commerce
Bibliographic record
Abstract
The papers in these proceedings represent the technical contributions to the 11th ACM Conference on Electronic Commerce -- EC'10, held June 7-11, 2010, at Harvard University in Cambridge, Massachusetts, USA. Since its inception in 1999, ACM EC has served as the leading scientific conference on advances in theory, systems, and applications for electronic commerce. The natural focus of the conference is on computer science issues, but the conference is interdisciplinary in nature, addressing a number of facets of electronic commerce including (1) theory and foundations; (2) languages; (3) automation, personalization, and targeting; (4) security, privacy, encryption, and digital rights; (5) applications and empirical studies; and (6) social factors. In addition to the main technical program, EC'10 features two workshops, four tutorials, and invited keynote presentations from MIT Sloan School of Management Professor Erik Brynjolfsson and Microsoft's General Manager for Experimentation Platform Ronny Kohavi. EC'10 is also co-located this year with the 9th Workshop on the Economics of Information Security (WEIS 2010), the Trading Agent Competition (TAC 2010) and the 25th IEEE Conference on Computational Complexity (CCC 2010). The call for papers attracted 136 submissions from authors in academia and industry from around the world, including Africa, Asia, Canada, Europe, the Middle East, and the United States. Each paper was reviewed by at least three program committee members and one senior program committee member on the basis of scientific novelty, technical quality, and importance to the field. After discussion and deliberation among the program committee, senior program committee and program chairs, 45 papers were selected for presentation at the conference; most of these papers appear in these proceedings. For a few accepted papers and at the authors' request, only abstracts are included along with pointers to full versions of these working papers. This accommodates the practices of fields outside of computer science in which journal rather than conference publishing is the norm and conference publishing sometimes precludes journal publishing. It is expected that many of the papers in these proceedings will appear in a more polished and complete form in scientific journals in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.229 | 0.114 |
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".