Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Bibliographic record
Abstract
Welcome to Toronto! We are pleased to host the Annual International ACM SIGIR Conference on its second visit to Canada. The tutorials, keynote speech, papers, posters, demos and workshops to be given over the next five days represent current techniques, challenges, and advances in information retrieval.Since the 8th SIGIR Conference in Montreal, 1985, information retrieval applications have become ubiquitous. It is difficult to imagine using a personal computer, a library, the web, or a peer-to-peer file sharing system without relying on the results of information retrieval research. At the same time it is easy to observe limitations in the tools we use and to imagine how they might be improved. These observations provide the impetus for current and future research.Toronto, Canada's largest city with a population of 2.5 million, is home to virtually all of the world's cultural groups, boasting safe and clean streets, first class entertainment, fine dining, major league sports, parks, and recreation facilities. It may surprise you that Toronto is also a major centre for television and movie production, third in North America after Los Angeles and New York. Chicago -winner of six Academy Awards including Best Picture - was filmed here.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.057 |
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".