Selection of Models of Lagged Identification Rates and Lagged Association Rates Using AIC and QAIC
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
The lagged identification rate is the probability of identifying an individual given its identification some time lag earlier. The lagged association rate is the probability that two individuals are associated given their association some time lag earlier. Models of lagged identification and association rates fit by maximizing the sums of non independent log-likelihoods have approximately unbiased parameter estimates. Simulations suggest that: Akaike-Information-Criterion often selects the true model of lagged identification rate data; quasi-Akaike-Information-Criterion performs better for lagged association rates; and confidence intervals for parameters are best obtained by bootstrap methods for lagged identification rates and quasi-likelihood or jackknife methods for lagged association rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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