Rater-Based Assessments as Social Judgments: Rethinking the Etiology of Rater Errors
Bibliographic record
Abstract
BACKGROUND: Measurement errors are a limitation of using rater-based assessments that are commonly attributed to rater errors. Solutions targeting rater subjectivity have been largely unsuccessful. METHOD: This critical review examines investigations of rater idiosyncrasy from impression formation literatures to ask new questions for the parallel problem in rater-based assessments. RESULTS: Raters may form categorical judgments about ratees as part of impression formation. Although categorization can be idiosyncratic, raters tend to consistently construct one of a few possible interpretations of each ratee. If raters naturally form categorical judgments, an assessment system requiring ordinal or interval ratings may inadvertently introduce conversion errors due to translation techniques unique to each rater. CONCLUSIONS: Potential implications of raters forming differing categorizations of ratees combined with the use of rating scales to collect categorical judgments on measurement outcomes in rater-based assessments are explored.
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 | 0.451 | 0.650 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".