Model-Based Clustering and Prediction With Mixed Measurements Involving Surrogate Classifiers
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
Identification of underlying subpopulations to account for unobserved heterogeneity in the population is a challenging statistical problem, mainly because no explicit information about the latent classes is available. Although latent class analysis via finite mixture models is often used successfully to probabilistically identify subpopulations in applications, it often fails with data for which such subpopulations exhibit high latency. Borrowing strength from readily accessible auxiliary classifiers, even when subject to misclassification, may yield improved results in such settings. We develop in this article a joint modeling approach that combines data from multiple sources, including observed characteristics that are often used alone for clustering and classification, as well as results based on imperfect surrogate classifiers, to better identify the latent classes for more accurate classification and prediction. We outline maximum likelihood estimation for the joint model using the EM algorithm, and we show empirically via simulations that our methodology yields better estimates of the underlying latent class distributions than those obtained by ignoring the auxiliary information, while providing joint assessments of the surrogate classifiers. The advantages are significant when there is high latency and the surrogate classifiers are at least moderately accurate. We use real diagnostic data on dry eye disease, for which no gold standard is available, to illustrate our methodology.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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