Representing Unique Stakeholder Perspectives in BPM Notations
Bibliographic record
Abstract
Evidence shows that proposals for new modeling notations emerge and evolution of current ones are becoming more complex, often in an attempt to satisfy the many different modeling perspectives required by each stakeholder. This paper presents a method to identify the specific notation construct requirements, at multiple levels of abstraction, which satisfy the needs of a stakeholder when performing a specific task. Initially the focus is on two different stakeholders: software engineers (SE) and business analysts(BA), and one specific software engineering activity: requirements eliciting and analysis. The specific body of knowledge of the two stakeholders (Software Engineering Book of Knowledge (SWEBOK) for the SE, and Business Analysis Body of Knowledge (BABOK) for the BA) are used to identify each stakeholder specific notation construct requirements, at multiple levels of abstraction, in order to propose a simplification of their notation and constructs set. This paper presents solution avenues to simplify business process modeling notations by identifying the specific constructs preferred by different stakeholders.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".