Proceedings of the 2005 ACM SIGPLAN international workshop on Types in languages design and implementation
Bibliographic record
Abstract
It is our great pleasure to welcome you to the second ACM SIGPLAN Workshop on Types in Language Design and Implementation -- TLDI'05. Although only the second workshop by this name, the TLDI workshops are a continuation of the Types in Compilation -- TIC workshops, the first of which was held in 1997. This series of workshops brings together researchers from the many areas influenced by types and proofs. The role of types and proofs in all aspects of language design, compiler construction, and software development has expanded greatly in recent years. Type systems, type analyses, and formal deduction have led to new concepts in compilation techniques for modern programming languages, verification of safety and security properties of programs, program transformation and optimization, and many other areas. The mission of the workshops is to bring together researchers in all these areas to share novel ideas and stimulate interaction and discussion on the ever expanding use of types.The call for papers attracted 23 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 10 papers that cover a variety of topics, such as type inference, shared memory synchronization, low-level target languages, resource bounds checking, and information flow.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.076 | 0.023 |
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".