An Evaluation of Interrater Reliability Measures on Binary Tasks Using <i>d-Prime</i>
Bibliographic record
Abstract
Many indices of interrater agreement on binary tasks have been proposed to assess reliability, but none has escaped criticism. In a series of Monte Carlo simulations, five such indices were evaluated using d-prime, an unbiased indicator of raters’ ability to distinguish between the true presence or absence of the characteristic being judged. Phi and, to a lesser extent, Kappa coefficients performed best across variations in characteristic prevalence, and raters’ expertise and bias. Correlations with d-prime for Percentage Agreement, Scott’s Pi, and Gwet’s AC 1 were markedly lower. In situations where two raters make a series of binary judgments, the findings suggest that researchers should choose Phi or Kappa to assess interrater agreement as the superiority of these indices was least influenced by variations in the decision environment and characteristics of the decision makers.
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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 | 0.289 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".