Experiences on the Belief-Theoretic Integration of Para-consistent Conceptual Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Viewpoint-based conceptual modeling is concerned with the identification of a complete and coherent set of software models that have been developed with the involvement of various analysts. The contribution of multiple analysts in this process will provide a rich and comprehensive final product. One of the major concerns in any process requiring the direct involvement of human analysts is the introduction of uncertainty and inconsistency. In this paper, we employ a formal model based on belief theory that attempts to capture the degree of analysts' uncertainty towards their specifications and builds on these information to create a unique integrated model. The model is employed in the process of developing a conceptual model for the Pet Store application. The results show that the formal framework provides suitable tools for formal negotiation, belief revision, consensus building, belief recommendation and expert reliability evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it