Additive Plausibility Characterizes the Supports of Consistent Assessments
Bibliographic record
Abstract
Abstract. We introduce three definitions. First, we let a “base-ment ” be a set of nodes and actions that supports at least one assessment. Second, we derive from an arbitrary basement its im-plied “plausibility ” (i.e. infinite-relative-likelihood) relation among the game’s nodes. Third, we say that this plausibility relation is “additive ” if it has a completion represented by the nodal sums of a mass function defined over the game’s actions. This last con-struction is built upon Streufert (2012)’s result that nodes can be specified as sets of actions. Our central result is that a basement has additive plausibility if and only if it supports at least one consistent assessment. The result’s proof parallels the early foundations of probability the-ory and requires only Farkas ’ Lemma. The result leads to related characterizations, to an easily tested necessary condition for con-sistency, and to the repair of a nontrivial gap in a proof of Kreps and Wilson (1982). 1.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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; both teacher heads agree on what is shown here.
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".