The sufficiency of the evidence, the relevancy of the evidence, and quantifying both with a single number
Bibliographic record
Abstract
Consider a data set as a body of evidence that might confirm or disconfirm a hypothesis about a parameter value. If the posterior probability of the hypothesis is high enough, then the truth of the hypothesis is accepted for some purpose such as reporting a new discovery. In that way, the posterior probability measures the sufficiency of the evidence for accepting the hypothesis. It would only follow that the evidence is relevant to the hypothesis if the prior probability were not already high enough for acceptance. A measure of the relevancy of the evidence is the Bayes factor since it is the ratio of the posterior odds to the prior odds. Measures of the sufficiency of the evidence and measures of the relevancy of the evidence are not mutually exclusive. An example falling in both classes is the likelihood ratio statistic, perhaps based on a pseudolikelihood function that eliminates nuisance parameters. There is a sense in which the likelihood ratio statistic measures both the sufficiency of the evidence and its relevancy. That result is established by representing the likelihood ratio statistic in terms of a conditional possibility measure that satisfies logical coherence rather than probabilistic coherence.
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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 | 0.081 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".