OO-IP hybrid language design and a framework approach to the GIPC
Bibliographic record
Abstract
Intensional Programming is a declarative programming paradigm in which expressions are evaluated in an inherently multidimensional context space. The Lucid family of programming languages is, to this day, the only programming languages of true intensional nature. Lucid being a functional language, Lucid programs are inherently parallel and their parallelism can be efficiently exploited by the adjunction of a procedural language to increase the granularity of its parallelism, forming hybrid Lucid languages. That very wide array of possibilities raises the need for an extremely flexible programming language investigation platform to investigate on this plethora of possibilities for Intensional Programming. That is the purpose of the General Intensional Programming System (GIPSY), especially, the General Intensional Programming Compiler (GIPC) component. The modularity, reusability and extensibility aspects of the framework approach make it an obvious candidate for the development of the GIPC. The framework presented in this thesis provides a better solution compared to all other techniques used to this day to implement the different variants of intensional programming. Because of the functionality of hybrid programming support in the GIPC framework, a new OO-IP hybrid language is designed for further research. This new hybrid language combines the essential characteristics of IPL and Java, and introduces the notion of object streams which makes it is possible that each element in an IPL stream could be an object with embedded intensional properties. Interestingly, this hybrid language also brings to Java objects the power which can explicitly express context, creating the novel concept of intensional objects, Le. objects whose evaluation is context-dependent, which are therein demonstrated to be translatable into standard objects. By this new feature, we extend the use and meaning of the notion of object and enrich the meaning of stream in IPL and semantics of Java. At the same time, during the procedure to introduce intensional objects and this OO-IP hybrid language, many factors are considered. These factors include how to integrate the new language with the GIPC framework design and the issues related to its integration in the current GIPSY implementation. Current semantic rules show that the new language can work well with the GIPC framework and the GIPSY implementation, which is another proof of the validity of our GIPC framework design. Ultimately, the proposed design is put into implementation in the GIPSY and the implementation put to test using programs from different application domains written in this new OO-IP language
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 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".