A Distance-Based GRL Approach to Goal Model Refinement and Alternative Selection
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
Multi-Criteria Decision Analysis (MCDA) has been used widely to guide decision making in multi-attribute selection problems. Few studies have however proposed the use of combinations of MCDA approaches with goal-oriented modeling to rank design alternatives related to business process models. We propose a distance-based approach for models specified with the Goal-oriented Requirement Language (GRL) that guides the selection of alternatives, with a focus on business process integration. This approach, named DbGRL, exploits the Analytic Hierarchy Process (AHP) technique and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) for building and analyzing GRL models. DbGRL provides stakeholders/decision-makers with insights into the changes needed in the GRL model or related (process) models to achieve desired outcomes. It also identifies ideal and anti-ideal points used to investigate how close or how far the alternatives are from those points. This paper presents DbGRL and its usage with a simple but realistic healthcare-related example where results are promising. DbGRL's expected benefits and limitations are also discussed.
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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) | 0.000 | 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 | 0.001 | 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