Are Secondary Assessors Uncertain When They Disagree About Relevance Judgements?
Bibliographic record
Abstract
The collection of relevance judgements by assessors is important for many information retrieval (IR) tasks. In addition to the construction of test collections, relevance judging is critical to e-discovery and other applications where many assessors are hired to perform relevance judging. It is well known that assessors may differ in their judgements for a given document. One possible cause of a judgement difference is that an assessor may be uncertain in their judgement and thus may in effect be guessing the document's relevance. If assessors are aware of their uncertainty and can self-report their level of certainty, then uncertain relevance judgements can be targeted for adjudication by additional assessors. In this paper, we conducted a user study with 48 participants to test our hypothesis that assessors will be uncertain about their relevance judgements when the assessors are likely to disagree with each other. We found that for low consensus documents, i.e. documents known for assessor disagreement, assessors judge these documents with almost as much certainty as high consensus documents. In particular, assessor self-reported uncertainty is predictive of disagreement only for high consensus documents and not for low consensus documents.
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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 | 0.145 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".