Challenge Paper: Marginal Probabilities for Instances and Classes (Poster Presentation SRL Workshop)
Bibliographic record
Abstract
In classic AI research on combining logic and probability, Halpern introduced an inference principle for marginal probabilities of ground atoms: If the corresponding population frequency is known, the marginal probability should be equal to it. For instance, if the only thing we know about Tweety is that Tweety is a bird, then the probability that Tweety flies should be the frequency of flyers in the class of birds. We provide several arguments for why a statistical-relational inference system should satisfy Halpern’s principle. If the principle is accepted, the technical challenge is then to construct inference models to meet this specification. The technical part of the paper gives examples of structures and parameterizations that do and do not satisfy the constraint on marginal probabilities, using Parametrized Bayes nets. 1.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.038 | 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".