Proceedings of the 9th ACM SIGPLAN International Workshop on Type-Driven Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it