Searching the Boundaries of a Modeling Space to Test Metamodels
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
Model-driven software development relies on metamodels to formally capture modeling spaces. Metamodels specify concepts and relationships between them in order to represent either a specific business domain model or the input and output domains for operations on models (e.g., model refinement). In all cases, a metamodel is a finite description of a possibly infinite set of models, i.e. the set of all models which structure conforms to the description specified in the metamodel. However, there is currently no systematic method to test that a metamodel captures all the correct models of the domain and no more. In this paper, we focus on the automatic selection of a set of models in the modeling space captured by a metamodel. The selected set should both cover as many representative situations as possible and be kept small as possible for further manual analysis. We use simulated annealing to select a set of models that satisfies those two objectives and report on results using two metamodels from two different domains.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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