A qualitative exploration of secondary assessor relevance judging behavior
Bibliographic record
Abstract
Secondary assessors frequently differ in their relevance judgments. Primary assessors are those that originate a search topic and whose judgments truly reflect the assessor's relevance criteria. Secondary assessors do not originate the search and must instead attempt to make relevance judgments based on a description of what is and is not relevant. Secondary assessors may be hired to help in the construction of test collections. Currently our knowledge about secondary assessors is largely limited to quantitative measurements of the differences between judgments produced by secondary and primary assessors. In order to better understand the behavior of secondary assessors, we conducted a think-aloud study of secondary assessing behavior. We asked secondary assessors to think-aloud their thoughts as they judged documents. The think-aloud method gives us insight into how relevance decisions are made. We found that assessors are not always certain in their judgments. In the extreme, secondary assessors are forced to make guesses concerning the relevance of documents. We present many reasons and examples of why secondary assessors produce differing relevance judgments. These differences result from the interactions between the search topic, the secondary assessor, the document being judged, and can even apparently be caused by a primary assessor's error in judging relevance. To improve the quality of secondary assessor judgments, we recommend that relevance assessing systems allow for the collection of assessor's certainty and provide a means to help assessors efficiently express their judgment rationale.
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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".