Time-Limits and Summaries for Faster Relevance Assessing
Bibliographic record
Abstract
Relevance assessing is a critical part of test collection construction as well as applications such as high-recall retrieval that require large amounts of relevance feedback. In these applications, tens of thousands of relevance assessments are required and assessing costs are directly related to the speed at which assessments are made. We conducted a user study with 60 participants where we investigated the impact of time limits (15, 30, and 60 seconds) and document size (full length vs. short summaries) on relevance assessing. Participants were shown either full documents or document summaries that they had to judge within a 15, 30, or 60 seconds time constraint per document. We found that using a time limit as short as 15 seconds or judging document summaries in place of full documents could significantly speed judging without significantly affecting judging quality. Participants found judging document summaries with a 60 second time limit to be the easiest and best experience of the six conditions. While time limits may speed judging, the same speed benefits can be had with high quality document summaries while providing an improved judging experience for assessors.
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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 | 0.007 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".