Bibliographic record
Abstract
Intensional logic is the mathematical foundation for Intensional Programming Languages (IPL). Lucid, initially founded on the dataflow paradigm, embraced intensional logic, and became a multi-dimensional intensional programming language. In all these developments context was the core concept. In its becoming an IPL, Lucid implicitly absorbed the notion of context, allowing expressions to be evaluated at different contexts. However, context cannot be explicitly named and manipulated in the current versions of Lucid. This restricts the ability of Lucid to be an effective programming language for programming diverse applications. This thesis discusses the extension of Lucid with contexts as a first class object. That is, contexts can be defined, assigned values, used in expressions, and passed as function parameters. The language thus extended, is called Lucx (Lucid extended with cbontexbts )(the x is used as the x in Latex). A context theory is developed to provide a semantic basis for context manipulation in Lucx. That is, contexts, context operators, and a context calculus are formally defined, and the formal syntax and semantics of Lucx are also given. The benefits achieved by such an extension are illustrated by applying the extended language to program different applications including Timed Systems, Agent Communication, Constraint Programming, and in the formal development of context-aware systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".