SIGNED LIKELIHOOD ROOT WITH A SIMPLE SKEWNESS CORRECTION: REGULAR MODELS, SECOND ORDER
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
SUMMARY A standardized maximum likelihood departure, a standardized score departure, the signed likelihood root: these are familiar inference outputs from statistical packages, with the signed likelihood root often viewed as the most reliable. A third-order adjusted signed likelihood root called r is available from likelihood theory, but the formulas and development methods are not always easily implemented. We use a log-model Taylor expansion to develop a simple second order adjustment to the signed likelihood root, an adjustment that is easy to calculate and easy to explain, and easy to motivate. The theory is developed, simulations are recorded to indicate repetition accuracy, real data are analyzed, and connections to alternatives are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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