Proceedings of the 9th annual ACM international workshop on Web information and data management
Bibliographic record
Abstract
The ACM CIKM07 Workshop on Information and Management (WIDM 2007) is the ninth in a series of workshops on Information and Management held in conjunction with the International Conference on Information and Knowledge Management (CIKM). The objective of the workshop is to bring together researchers, industrial practitioners, and developers to study how information can be extracted, stored, analyzed, and processed to provide useful knowledge to the end users for various advanced database applications. We hope that these proceedings will serve as a valuable reference for all experts in the field. The call for papers resulted in the submission of 80 papers from 32 countries: Australia, Austria, Belgium, Brazil, Canada, Chile, China, Cuba, Cyprus, Czech Republic, France, Germany, Greece, India, Iran, Israel, Italy, Japan, Mauritius, Mexico, Netherlands, Norway, Pakistan, Poland, Portugal, Singapore, South Korea, Sweden, Switzerland, Taiwan, United Kingdom and the United States. Starting from 2005, the workshop has a one-day schedule. All papers were thoroughly reviewed by the program committee and external reviewers. This year, the program committee accepted 20 papers, resulting in a competitive 25% acceptance rate. The 20 accepted papers were divided into five sessions: XML and Semi-Structured Data, P2P and System Design Issues, Personalization, Mining Knowledge from Data and Web Metadata and Search.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".