Bibliographic record
Abstract
We present a framework for offline partial evaluation for call-by-value functional programming languages with an ML-style typing discipline. This includes a binding-time analysis which is (1) polymorphic with respect to binding times; (2) allows the use of polymorphic recursion with respect to binding times; (3) is applicable to a polymorphically typed term; and (4) is proven correct with respect to a novel small-step specialization semantics.The main innovation is to build the analysis on top of the region calculus of Tofte and Talpin [1994], thus leveraging the tools and techniques developed for it. Our approach factorizes the binding-time analysis into region inference and a subsequent constraint analysis. The key insight underlying our framework is to consider binding times as properties of regions.Specialization is specified as a small-step semantics, building on previous work on syntactic-type soundness results for the region calculus. Using similar syntactic proof techniques, we prove soundness of the binding-time analysis with respect to the specializer. In addition, we prove that specialization preserves the call-by-value semantics of the region calculus by showing that the reductions of the specializer are contextual equivalences in the region calculus.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".