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
A software modification task often addresses several concerns . A concern is anything a stakeholder may want to consider as a conceptual unit, including features, nonfunctional requirements, and design idioms. In many cases, the source code implementing a concern is not encapsulated in a single programming language module, and is instead scattered and tangled throughout a system. Inadequate separation of concerns increases the difficulty of evolving software in a correct and cost-effective manner. To make it easier to modify concerns that are not well modularized, we propose an approach in which the implementation of concerns is documented in artifacts, called concern graphs. Concern graphs are abstract models that describe which parts of the source code are relevant to different concerns. We present a formal model for concern graphs and the tool support we developed to enable software developers to create and use concern graphs during software evolution tasks. We report on five empirical studies, providing evidence that concern graphs support views and operations that facilitate the task of modifying the code implementing scattered concerns, are cost-effective to create and use, and robust enough to be used with different versions of a software system.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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