Proceedings of the 19th ACM international conference on Information and knowledge management
Bibliographic record
Abstract
On behalf of the organizing committee, I wholeheartedly welcome you to the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010). I hope this conference proves to be interesting and beneficial. CIKM is a well-known top tier and premier ACM conference in the areas of information retrieval, knowledge management and database. Since its inception, the CIKM conference has provided a unique international forum for the presentation, discussion, and dissemination of research findings in data management, information retrieval, and knowledge management. The purpose of the conference is to identify challenging problems facing the development of future knowledge and information systems, and to shape future research directions through the publication of high quality, applied and theoretical research findings. The conference has been a leading forum in which experts from academia, industry, and the government gather to exchange ideas, research achievements, and technical developments in multidisciplinary research areas. CIKM has rapidly grown to become one of the world's most recognized conferences in the field. This year CIKM has received a record high number of submissions in the history of CIKM, as can be seen from the following statistics: 1382 abstracts submitted 945 full papers plus 38 demo papers submitted 126 papers accepted for presentation as full papers (13.3% acceptance rate) and an additional 165 were accepted for short papers (17.5%). In addition to regular research tracks, CIKM 2010 features 4 keynote speakers, 4 pre-conference tutorials, 9 workshops, 12 industrial full papers and 20 demo papers. I am proud of our program and acknowledge the tireless efforts of people who materialized this program. First of all, I am honored to have 4 distinguished keynote speakers: Jamie Callan, Susan Dumais, Gregory Grefenstette, and Divesh Srivastava. I deeply appreciate their time and commitment to deliver their speeches and share their cutting-edge research experiences and insightful comments in their research topics.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.134 | 0.094 |
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".