Preface to the 1st International Hands-on Workshop on Collaborative Modeling (HoWCoM 2021)
Bibliographic record
Abstract
The ability to collaboratively engineer models of systems has become a particularly important topic in Model-Driven Engineering (MDE). It is due to the increasing complexity of nowadays’ systems that their engineering requires a coordinated interplay between stakeholders. Collaboration is often seen as an enabling technique, and a tool-related aspect in MDE. Yet, collaborative modeling has typically been addressed at the foundations level. Collaborative MDE tools have not been in the focus of any scientific event so far. Given the recent trends in the research and application of collaborative MDE, especially considering that collaborative MDE has become a prominent part of relevant industrial R&D projects, we found that it was important to organize a workshop that would allow us to put tools in the spotlight and evaluate them from a practical standpoint. This workshop intended to leverage a rare opportunity provided by the online format of the 2021 edition of MoDELS. The online format enabled studying the dynamics of collaborative modeling endeavors in a realistic environment, with physically distanced users forced to rely on the means of collaboration provided by the tools under study.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.186 | 0.108 |
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".