Evolution mechanisms for goal-driven pattern families used in business process modelling
Why this work is in the frame
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Bibliographic record
Abstract
Reusability is important when modelling business processes and patterns are known to increase reusability. A pattern-based framework that lays down a foundation for capturing knowledge about business goals and processes and customising it for specific organisations in a given domain is hence valuable. For many reasons however, the problems and solutions within a business domain are constantly changing. Consequently, such framework can be useful only if it provides mechanisms for evolving pattern families over time. In this paper, we propose and formalise four mechanisms for evolving a goal-driven pattern family targeting the modelling of business goals, business processes, and traceability links between these two views. We demonstrate the feasibility of the evolution algorithms with examples from the patient safety domain. We also illustrate how the framework is used to extract relevant business goals and processes in a specific healthcare context.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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