A general goodness‐of‐fit approach for inference procedures concerning the kappa statistic
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Bibliographic record
Abstract
The kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a chi-square goodness-of-fit test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it