Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 it