Proceedings of the 13th workshop on Aspect-oriented modeling
Bibliographic record
Abstract
New and powerful aspect-oriented programming techniques are presented at many international venues every year. However, it is not clear what features of such techniques are common aspect-oriented concepts and what features are rather language-specific specialties. Research in aspect-oriented modeling has the potential to help find such common characteristics from a perspective that is at a more abstract level (i.e., programming language-independent). The Aspect-Oriented Modeling (AOM) Workshops, now in its 13th edition, bring together researchers and practitioners from two communities: aspect-oriented software development (AOSD) and software model engineering. The workshops provide a forum for presenting new ideas and discussing the state of research and practice in modeling various kinds of crosscutting concerns at different levels of abstraction. The goals of the workshops are to identify and discuss the impacts of aspect-oriented technologies on model engineering. Specifically, the workshop seeks to provide aspect-oriented software developers with general modeling means to express aspects and their crosscutting relationships onto other software artifacts. The aspect-oriented modeling workshops occur twice a year as satellite events of the AOSD and MoDELS conferences. These workshops receive a strong following among researchers in both communities with activie participation that has helped to unite these two areas over the past seven years. More details are available at: http://www.aspect-modeling.org.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".