Bibliographic record
Abstract
Aspect-oriented modelling approaches, e.g. the multi-view modelling approach Reusable Aspect Models (RAM), advocate to model concerns separately, and then to use model composition to create complex models in which these concerns are intertwined. In such a context, specifying the composition of the models is a non-trivial task, in particular when it comes to specifying the composition of behavioural models. This is the case for RAM message views, which define behaviour using sequence diagrams. In this paper we describe how we added an additional behavioural view to RAM -- the state view -- that specifies the allowed invocation protocol of class instances.. We discuss why Protocol Modelling, a compositional modelling approach based on state diagrams, is an ideal notation to specify such a state view, and show how we added support for protocol modelling to the RAM metamodel. Finally, we demonstrate how to model using the new state views by means of an example, and explain how state views can be exploited to verify the correctness of compositions.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".