Proceedings of the 4th ACM SIGPLAN international conference on Principles and practice of declarative programming
Bibliographic record
Abstract
Together with the Seventh International ACM SIGPLAN Conference on Functional Programming (ICFP'02), the Generative Programming and Component Engineering Conference (GPCE'02) and a number of associated workshops, PPDP'02 has formed a federation of conferences known as Colloquium on Principles, Logics, and Implementations of high-level programming languages (PLI 2002). Previous PLI colloquia were held in Paris, in September 1999, in Montreal, in September 2000 and in Firenze, in September 2001. These events are organized by SIGPLAN, ACM's Special Interest Group on Programming Languages.PPDP aims to stimulate research on the use of declarative methods in programming and on the design, implementation and application of programming languages that support such methods. Topics of interest include any aspect related to understanding, integrating and extending programming paradigms such as those for logic, functional, constraint, probabilistic, rule and object-oriented programming; concurrent extensions and mobile computing; type theory; support for modularity; use of logical methods in the design of program development tools; program analysis and verification; abstract interpretation; development of implementation methods; application of the relevant paradigms and associated methods in industry and education.A total of 36 submissions (33 regular papers and 3 system descriptions) were received in response to the call for papers. The ConfMan conference management system was used for the handling of electronic submissions, for allocation of reviewing duties, and for filing of reviews. The program committee meeting was conducted electronically. Each paper was reviewed by at least three referees and 18 papers including 2 system presentations were selected for publication.Because practice has an important place in the conference, we decided to give to system presentations a similar place in the proceedings and in the program than to the regular papers.In addition to the selected presentations, the scientific program included three invited talks, by Neil Jones (University of Copenhagen), Catuscia Palamidessi (The Pennsylvania State University), and Janos Sztipanovits (Vanderbilt University).
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.035 |
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".