Parametric polymorphism for software component architectures
Bibliographic record
Abstract
Parametric polymorphism has become a common feature of mainstream programming languages, but software component architectures have lagged behind and do not support it. We examine the problem of providing parametric polymorphism with components combined from different programming languages. We have investigated how to resolve different binding times and parametrization semantics in a range of representative languages and have identified a common ground that can be suitably mapped to different language bindings. We present a generic component architecture extension that provides support for parameterized components and that can be easily adapted to work on top of various software component architectures in use today (e.g., corba, dcom, jni). We have implemented and tested this architecture on top of corba. We also present Generic Interface Definition Language (gidl), an extension to corba-idl supporting generic types and we describe language bindings for C++, Java and Aldor. We explain our implementation of gidl, consisting of a gidl to idl compiler and tools for generating linkage code under the language bindings. We demonstrate how this architecture can be used to access C++'s stl and Aldor's BasicMath libraries in a multi-language environment and discuss our mappings in the context of automatic library interface generation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".