A model-driven framework for representing and applying design patterns
Bibliographic record
Abstract
Design patterns encode proven solutions to recurring design problems. To use a design pattern properly, we need to 1) understand the design problem the pattern resolves, 2) recognize an instance of this problem in the model at hand, and 3) to transform the model to produce the proposed solution. We argue that an explicit representation of the design problem solved by a pattern is key to supporting each one of these tasks. We propose to represent a design pattern using a triple (MP, MS, T) where MP is a model of the design problem solved by the pattern, MS is a model of the solution proposed by it, and T is a rule-based representation of the transformations embodied in the application of the pattern. In this paper, we describe the principles underlying our approach and the current implementation using the Eclipse Modeling FrameworkTMand JRulesTM.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".