Delaying decisions in variable concern hierarchies
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
Concern-Oriented Reuse (CORE) proposes a new way of structuring model-driven software development, where models of the system are modularized by domains of abstraction within units of reuse called concerns. Within a CORE concern, models are further decomposed and modularized by features. This paper extends CORE with a technique that enables developers of high-level concerns to reuse lower-level concerns without unnecessarily committing to a specific feature selection. The developer can select the functionality that is minimally needed to continue development, and reexpose relevant alternative lower-level features of the reused concern in the reusing concern's interface. This effectively delays decision making about alternative functionality until the higher-level reuse context, where more detailed requirements are known and further decisions can be made. The paper describes the algorithms for composing the variation (i.e., feature and impact models), customization, and usage interfaces of a concern, as well as the concern's realization models and finally an entire concern hierarchy, as is necessary to support delayed decision making in CORE.
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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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