Bibliographic record
Abstract
There is an increasing need for high-quality software components. Reusable components and formal specifications are two complementary and promising approaches to achieve this goal. One method for enhancing the reusability of existing components is generalization that creates generic components by parameterizing specific ones. Generalization and instantiation are two methods related respectively to the development for reuse and development with reuse. Generalization, that is the abstraction of existing components, identifies commonalities across a class of entities, while instantiation customizes the general properties under different circumstances. In this paper, we present several generalization and instantiation algorithms for algebraic specifications. A major difficulty during the generalization process is determining the appropriate level of generality. Highly specific components have little chance of being reused. Meanwhile, if a component is too general, its reuse might also be hard. Therefore, we introduce a novel method based on the categorized constructors to control the level of abstraction in generic components with the goal of producing effective reusable components. Through a medium-scale example, the generalization and instantiation operations are illustrated in detail.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".