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
Requirements engineering techniques that explicitly recognize the importance of clearly identifying and treating crosscutting concerns are called Aspect-oriented Requirements Engineering Approaches (AORE approaches). The emergence of aspect-oriented programming languages has raised the explicit need to identify crosscutting concerns already during the analysis phase. Besides this observation, the modular representation of crosscutting requirements is a first step to ensure traceability of crosscutting concerns through all other artifacts of the software lifecycle (architecture, design and implementation).Aspect-oriented requirements engineering approaches improve existing requirements engineering approaches through an explicit representation (and modularization) of concerns that were otherwise spread throughout other requirements artifacts (such as use cases, goal models, viewpoints, etc.).AORE approaches adopt the principle of separation of concerns at the analysis phase (the early separation of concerns). In other words, AORE approaches provide a representation of crosscutting concerns in requirements artifacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.001 | 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