Proceedings of the 3rd ACM SIGPLAN international conference on Principles and practice of declarative programming
Bibliographic record
Abstract
This volume contains the papers and abstracts presented at PPDP'01, the Third International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming, held in Florence, Italy, September 5-7, 2001.Together with the Sixth International ACM SIGPLAN Conference on Functional Programming (ICFP) and a number of associated workshops, PPDP'01 has formed a federation of conferences known as Colloquium on Principles, Logics, and Implementations of high-level programming languages (PLI 2001). Previous PLI colloquia were held in Paris, in September 1999, and in Montreal, in September 2000. These events are organised by SIGPLAN, ACM's Special Interest Group on Programming Languages.PPDP 2001 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 functional, logic, constraint 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 ofimplementation methods; and application of the relevant paradigms and associated methods in industry and education.A total of 40 submissions were received in response to the call for papers. The START 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 19 papers were selected for publication. In addition to the regular paper presentations, the scientific program included three invited talks, by Javier Esparza (University of Edinburgh), Andrew Gordon (Microsoft Research Cambridge), and Dave Schmidt (Kansas State University).
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 | 0.006 | 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) | 0.081 | 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".