On modeling interactions of early aspects with goals
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
Interactions in aspect-oriented models must be detected, documented, and resolved for aspects to be composed as desired. Generally, aspect interactions can be categorized as intrinsic (those that inherently exist among concerns) or technical (those that are dependent on technology and may change over time). Consequently, these types of interactions should be encapsulated properly. Goal models support reasoning about qualitative and quantitative relationships and are therefore ideally positioned to describe and reason about intrinsic interactions, because they are often of a qualitative nature. On the other hand, technical interactions are typically syntactic conflicts and dependencies which are modeled with different techniques. We present the Concern Interaction Graph (CIG), a goal model specialized for technical interactions in aspect-oriented models, which is integrated with other goal models for intrinsic concern interactions and stakeholder intentions. The CIG therefore allows global trade-offs among concerns that take intrinsic and technical interactions into account as well as the needs of stakeholders, while maintaining proper separation of concerns between intrinsic and technical interactions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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