A framework for multi-valued reasoning over inconsistent viewpoints
Bibliographic record
Abstract
During requirements elicitation, different stakeholders often hold different (and incompatible) views of how the proposed system should behave, resulting in inconsistencies between their descriptions. Consensus may not be needed for every detail, but it can be hard to determine whether a particular disagreement affects the critical properties of the system. Existing viewpoints-based frameworks support detection and resolution of inconsistencies, but do not support reasoning about the properties of inconsistent models. In this paper, we describe the bel framework for merging and reasoning about multiple, inconsistent state machine models. bel permits the analyst to choose how to combine information from the multiple viewpoints, where each viewpoint has an underlying multi-valued logic. The different values of our logics typically represent different levels of agreement. We have developed a multi-valued model checker, chek, that allows us to check the merged model against temporal prop...
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".