Formalizing Patterns with the User Requirements Notation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patterns need to be described and formalized in ways that enable the reader to determine whether the particular solution presented is useful and applicable to his or her problem in a given context. However, many pattern descriptions tend to focus on the solution to a problem, and not so much on how the various (and often conflicting) forces involved are balanced. This chapter describes the user requirements notation (URN), and demonstrates how it can be used to formalize patterns in a way that enables rigorous trade-off analysis while maintaining the genericity of the solution description. URN combines a graphical goal language, which can be used to capture forces and reason about trade-offs, and a graphical scenario language, which can be used to describe behavioral solutions in an abstract manner. Although each language can be used in isolation in pattern descriptions (and have been in the literature), the focus of this chapter is on their combined use. It includes examples of formalizing Design patterns with URN together with a process for trade-off analysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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