Incorporating Relevance in Epistemic States in Belief Revision
Bibliographic record
Abstract
We present an account of relevance in belief revision where, intuitively, one wants to only consider the relevant part of an agent's epistemic state in a revision.We assume that relevance is a domain-specific notion, and that (ir)relevance assertions are given as part of the agent's epistemic state.Such assertions apply in a given context, and are of the form "in the case that formula σ holds, the Y part of the agent's epistemic state is independent of the rest of the epistemic state", where Y is part of the signature of the language.Two approaches are given, one in which (in semantic terms) conditions are placed on a faithful ranking on possible worlds to enforce the (ir)relevance assertions, and a second in which the possible worlds characterising the agent's beliefs may be modified in a revision.These approaches are shown to yield the same resulting belief set.Corresponding postulates and a representation result are given.The overall approach is compared to that of Parikh's for language splitting as well as with multivalued dependencies in relational databases.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".