Report on the SIGIR 2015 Workshop on Reproducibility, Inexplicability, and Generalizability of Results (RIGOR)
Bibliographic record
Abstract
The SIGIR 2015 Workshop on Reproducibility, Inexplicability, and Generalizability of Results (RIGOR) took place on Thursday, August 13, 2015 in Santiago, Chile. The goal of the workshop was two fold. The first to provide a venue for the publication and presentation of negative results. The second was to provide a venue through which the authors of open source search engines could compare performance of indexing and searching on the same collections and on the same machines - encouraging the sharing of ideas and discoveries in a like-to-like environment. In total three papers were presented and seven systems participated.
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.
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 | 0.420 | 0.527 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.040 | 0.025 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".