Statistical Inferences For Interobserver Agreement Studies With Nominal Outcome Data
Bibliographic record
Abstract
Most statistical methods for interobserver agreement studies involving categorical data focus on dichotomous outcome variables, whereas only a limited number of methods have been developed for nominal outcome data. As a consequence, researchers may resort to dichotomization simply to facilitate the analysis of data, hence discarding potentially valuable information. We present three inference procedures for hypothesis testing concerning interobserver agreement studies with nominal data. These procedures may be applied to studies characterized by two observers and three or more outcome categories. We illustrate these methods by using previously published data sets.
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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 | 0.385 | 0.805 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".