Analysis of papers from twenty-five years of SIGIR conferences: What have we been doing for the last quarter of a century
Bibliographic record
Abstract
As part of the celebration of twenty-five years of ACM SIGIR conferences we performed a content analysis of all papers published in the proceedings of SIGIR conferences, including those from 2002. From this we determined, using information retrieval approaches of course, which topics had come and gone over the last two and a half decades, and which topics are currently “hot”. We also performed a co-authorship analysis among authors of the 853 SIGIR conference papers to determine which author is the most “central ” in terms of a co-authorship graph and is our equivalent of Paul Erdös in Mathematics. In the first section we report on the content analysis, leading to our prediction as to the most topical paper likely to appear at SIGIR2003. In the second section we present details of our co-authorship analysis, revealing who is the “Christopher Lee ” of SIGIR, and in the final section we give pointers to where readers who are SIGIR conference paper authors may find details of where they fit into the coauthorship graph. Content Analysis of SIGIR Conference Papers In order to determine what topic areas are appearing in the papers at the SIGIR
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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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.065 | 0.069 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".