An executable model for a family of election algorithms
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
Summary form only given. We present an executable model for a family of algorithms dealing with leader election in a ring topology. We follow the traditional approach of system family engineering. That is, we develop a feature model that captures variability across these algorithms. We then proceed to produce a generator. This generator receives as inputs specific values for each of the variation points (i.e., features) we identify. And it produces the behavior corresponding to the specific configuration of features at hand. Contrary to existing generative programming literature, we do not resort to C++ meta-programming but instead develop an executable model using Rational Rose RT. More precisely, we have designed a single state chart that can model all the algorithms of the family we studied. We focus here on how to obtain such a state chart, rather than on the identification of the features we used, or on ROSE-RT semantics. We do believe however that our approach can be reused to provide a semantically unified and executable modelling approach for other families of algorithms.
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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 | 0.000 | 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