Proceedings of the 9th ACM SIGPLAN International Workshop on Type-Driven Development
Bibliographic record
Abstract
Welcome to the 9th ACM SIGPLAN International Workshop on Type-Driven Development (TyDe 2024), co-located with the International Conference on Functional Programming (ICFP 2024).The workshop aims to discuss how static type information may be used effectively in the development of computer programs, bringing together leading researchers and practitioners who are using or exploring types as a means to support program development.The TyDe workshop was created by merging two previous workshops: the Workshop on Dependently Typed Programming and the Workshop on Generic Programming, thus combining two valuable research areas that combine both theory and practice having types as their foundation.Beyond these two pillars, other topics of interest include the design and implementation of strongly typed programming languages, building tooling and editor support that exploit type information, and using types in the derivation, calculation, or construction of programs.This year, the program of the workshop included 11 contributed talks and a keynote talk by Gabriele Keller of the University of Utrecht.The call for submissions sought both full papers (up to 12 pages, published in the ACM Digital Library) and extended abstracts (up to 3 pages, not formally published but posted on the workshop webpage).All submissions received (at least) three reviews and were evaluated as follows: all submissions for relevance and interest to the TyDe community, and full papers additionally for the novelty and significance of their results.We received 7 full papers and 6 extended abstracts, of which 6 and 5 were accepted, respectively.We would like to express our thanks to the contributors, the program committee members, the external reviewers, the TyDe steering committee, and the organizers of ICFP 2024.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 0.027 |
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".