Bibliographic record
Abstract
Gradual typing combines static and dynamic typing flexibly and safely in a single programming language. To do so, gradually typed languages implicitly insert casts where needed, to ensure at runtime that typing assumptions are not violated by untyped code. However, the implicit nature of cast insertion, especially on higher-order values, can jeopardize reliability and efficiency: higher-order casts can fail at any time, and are costly to execute. We propose Confined Gradual Typing, which extends gradual typing with two new type qualifiers that let programmers control the flow of values between the typed and the untyped worlds, and thereby trade some flexibility for more reliability and performance. We formally develop two variants of Confined Gradual Typing that capture different flexibility/guarantee tradeoffs. We report on the implementation of Confined Gradual Typing in Gradualtalk, a gradually-typed Smalltalk, which confirms the performance advantage of avoiding unwanted higher-order casts and the low overhead of the approach.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".