Elaborating evaluation-order polymorphism
Bibliographic record
Abstract
We classify programming languages according to evaluation order: each language fixes one evaluation order as the default, making it transparent to program in that evaluation order, and troublesome to program in the other. This paper develops a type system that is impartial with respect to evaluation order. Evaluation order is implicit in terms, and explicit in types, with by-value and by-name versions of type connectives. A form of intersection type quantifies over evaluation orders, describing code that is agnostic over (that is, polymorphic in) evaluation order. By allowing such generic code, programs can express the by-value and by-name versions of a computation without code duplication. We also formulate a type system that only has by-value connectives, plus a type that generalizes the difference between by-value and by-name connectives: it is either a suspension (by name) or a "no-op" (by value). We show a straightforward encoding of the impartial type system into the more economical one. Then we define an elaboration from the economical language to a call-by-value semantics, and prove that elaborating a well-typed source program, where evaluation order is implicit, produces a well-typed target program where evaluation order is explicit. We also prove a simulation between evaluation of the target program and reductions (either by-value or by-name) in the source program. Finally, we prove that typing, elaboration, and evaluation are faithful to the type annotations given in the source program: if the programmer only writes by-value types, no by-name reductions can occur at run time.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".