Formal methods in the scope of the Software and Systems Modeling journal
Bibliographic record
Abstract
Software and Systems Modeling (SoSyM) is a journal dedicated to advancing the field of software and systems modeling by publishing high-quality research that contributes to the theory and practice of modeling in software and systems engineering, which also includes processes executed automatically or involving humans.The journal aims to bridge the gap between academia and industry by fostering discussions on modeling languages, methodologies, tools, and their applications to real-world challenges.SoSyM encourages submissions that present innovative modeling approaches, their precise semantic foundations, empirical evaluations, and applications that have tangible impacts on software and system development processes.Given this mission, the journal welcomes research on formal methods, provided that such work is framed within the context of software and systems modeling.Formal methods, as mathematically rigorous techniques for specifying, developing, and verifying software and systems, undoubtedly have significant potential to enhance modeling practices.However, the focus of SoSyM is not formal methods in isolation but rather their role and contribution to the field of software and systems modeling.Thus, a manuscript that centers on a formal method must explicitly articulate its relevance to software and systems modeling.This means that a submission should not merely B
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".