Analyzing Heaped Counts Versus Longitudinal Presence/Absence Data in Joint Zero-inflated Discrete Regression Models
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
Multiple outcome recurrent event data are typical in social sciences, where several outcomes on an individual are collected. In situations where aggregated counts of events over a long observation period are recorded, rounding is common, leading to counts being heaped at rounded values. We consider situations where multiple outcome recurrent event data are recorded as binary responses indicating presence/absence of events between periodic assessments. By analyzing these jointly through linkage via random effects, we show that a joint outcome analysis of the presence/absence data, that are less prone to recall errors, provides high relative efficiency, compared to the analysis of true counts. Motivated by a study of criminal behavior, we demonstrate the utility of such joint analyses, including that the analysis of longitudinal presence/absence data eliminates the bias arising from the analysis of heaped count data, and hence incorrect conclusions concerning possible risk factors.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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