How can i* complement uml for modeling organizations?
Bibliographic record
Abstract
This paper presents a thorough investigation of the two popular enterprise modeling techniques: the UML-based, and the i*-based, and the research efforts undertaken using these modeling techniques. However, this paper does not provide a survey and/or explain these two enterprise-modeling techniques exhaustively. It aims at analyzing how these two techniques are complementary to each other and providing some guidance regarding selecting an appropriate modeling technique. Many interesting information that can help in deciding the modeling technique have been summarized in a comparison-table that we have developed. UML fails to address different challenges for modeling modern day enterprises, i* modeling language can address these issues by capturing the motivations, intents, and rationales behind the activities. However, i* cannot address "what" steps a process consists of, and "how" those steps are to be done as UML can address. Thus, i* complements UML in modeling a project
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.033 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".