Proceedings of the 15th workshop on Early aspects
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 15th Edition of the Early Aspects workshop. This year's workshop continues its tradition of being the premier forum for presentation of research results and tool demos on leading edge issues of identifying, representing, modularizing, and composing the crosscutting concerns in requirements engineering and architectural design. The theme of this year's workshop is learning from each other and from ourselves. The workshop gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various facets of early aspects. The call for papers attracted a dozen submissions from Asia, Europe, South America, and the United States. The program committee accepted 7 research papers that cover a variety of topics, including security concerns, business process modeling, architecture refactoring, model-driven development, and goal-oriented requirements analysis. In addition, the program includes a tool demo for identifying conflicting dependencies in requirements documents and a keynote speech by Mehmet Aksit from the University of Twente, The Netherlands. We hope that these proceedings will serve as a valuable reference for the early aspects researchers and practitioners.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.082 | 0.030 |
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".