SIGIR 2025 Low Resource Environments Track Report
Bibliographic record
Abstract
The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval introduced a new low resource environments (LREs) track, dedicated to build an information retrieval (IR) research community among scholars from low- and middle-income countries and those addressing IR challenges associated with limited resources. Rather than traditional research paper submissions, the track invited presentation proposals focused on information access in the context of LREs. The track had a full day schedule and was held on 16 July 2025 in Padua, Italy. This report provides the track's objectives, activities, lessons learned, and recommendations. Primary outcomes include: (i) participants from low-resource environments valued the opportunity to attend and present at SIGIR, with many expressing intent to submit a full paper at the conference; (ii) a need was identified for alternative, community-building activities to share knowledge, pool resources, and learn collectively; and (iii) early action is required to address visa and logistical challenges that may hinder participants from low income countries to participate in the conference. Date: 16 July 2025. Website: https://sigir2025.dei.unipd.it/low-resource-environments-track.html.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.155 | 0.154 |
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".