SIGNED LIKELIHOOD ROOT WITH A SIMPLE SKEWNESS CORRECTION: REGULAR MODELS, SECOND ORDER
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".