SIGIR 2025 Low Resource Environments Track Report
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
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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