View programming for decentralized development of OO programs
Bibliographic record
Abstract
There has been a lot of interest recently in the problem of building object oriented applications by somehow combining other application fragments that provide their own overlapping definitions or expectations of the same domain objects. We propose an approach based on the split objects model of prototype languages whereby an application object is represented by a varying set of instances-called views-that implement different parts of its domain behavior but that delegate its core functionalities to a core instance: an object's response to a message depends on the views currently attached to its core instance. Our approach is not purely prototype based in the sense that core instances and views are members of classes. Further, we recognize that the behavior inherent in views (classes) is often an adaptation of a generic behavior to the domain object at hand, and define viewpoints as parameterized class-like algebraic structures to embody such a generic behavior. We first describe view programming from the perspective of the developer. Next, we sketch a semi formal model of view programming, and describe the steps needed to implement it in a class based statically typed language, for instance, C++. Third, we look at the challenges and opportunities provided by view programming to support safe, robust, and efficient distributed applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".