A Distance-Based GRL Approach to Goal Model Refinement and Alternative Selection
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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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".