Towards a Declarative, Constraint-Oriented Semantics with a Generic Evaluation Algorithm for GRL
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
Abstract. Goal models described with the Goal-oriented Requirement Lan-guage (GRL) are amenable to various kinds of analyses, including quantitative and qualitative propagations of satisfaction values. However, most approaches use bottom-up evaluations involving operational semantics that can only answer what if questions. This paper introduces a new declarative semantics for GRL based on a constraint-oriented interpretation of goal models. This semantics en-ables constraint solvers to evaluate and optimize goal models in a way that is more generic than bottom-up and top-down propagation techniques, hence ena-bling other questions to be answered. A prototype that combines the jUCMNav modeling tool and the JaCoP constraint solver to support quantitative evalua-tions is used to demonstrate the feasibility and potential of this new approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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