The effect of collapsing multinomial data when assessing agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In epidemiological studies researchers often depend on proxies to obtain information when primary subjects are unavailable. However, relatively few studies have performed formal statistical inference to assess agreement among proxy informants and primary study subjects. In this paper, we consider inference procedures for studies of interobserver agreement characterized by two raters and three or more outcome categories. Of particular interest is the consequence of dichotomizing such data on the expected confidence interval width for the kappa coefficient. The effect of dichotomization on sample size requirements for testing hypotheses concerning kappa is also evaluated. METHODS: Simulation studies were used to compare coverage levels and widths for constructing confidence intervals. Sample size requirements were compared for multinomial and dichotomous data. We illustrate our results using a published data set on drinking habits that assesses agreement among primary and proxy respondents. RESULTS: Our results show that when multinomial data are treated as dichotomous, not only do the expected confidence interval widths become greater, but the penalty in terms of larger sample size requirements for hypothesis testing can be severe. CONCLUSION: We conclude that there are clear advantages in preserving multinomial data on the original scale rather than collapsing the data into a binary trait.
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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 | 0.057 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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