Compositional structured component model: handling selective functional composition
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
Software component technology has been promoted as an innovative means to tackle the issues of software reuse, software quality and, software development complexity. Several component models (CORBA, .Net, JavaBeans) have been introduced, yet certain issues and limitations inherent to components still need to be addressed. As software components with hosts of functionalities tend to be coarse to large-grained in size and since the set of functionalities required by an application varies according to the particular application context, an excessive number of unwanted functionalities might be generated by such components within the application. We present the compositional structured component model (CSCM) designed to handle the issue of unwanted component functionalities and to provide a flexible approach for easier customization, adaptation, and reuse. The CSCM model is designed to handle this issue via component functional composition using metadata composition instances, which allow selective composition of a component's required functionalities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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