Bibliographic record
Abstract
Multi-stage programming is a popular approach to typed meta-programming, reducing abstraction overhead and producing performant programs. However, the traditional quote-and-splice staging syntax, as introduced by Rowan Davies in 1996, can introduce complexities in managing expression evaluation, and also often necessitates sophisticated mechanisms for advanced features such as code pattern matching. This paper introduces λ ○▷ , a novel staging calculus featuring let-splice bindings, a construct that explicitly binds splice expressions to splice variables, providing flexibility in managing, sharing, and reusing splice computations. Inspired by contextual modal type theory, our type system associates types with a typing context to capture variables dependencies of splice variables. We demonstrate that this mechanism seamlessly scales to features like code pattern matching, by formalizing λ ○▷ pat , an extension of λ ○▷ with code pattern matching and rewriting. We establish the syntactic type soundness of both calculi. Furthermore, we define a denotational semantics using a Kripke-style model, and prove adequacy results. All proofs have been fully mechanized using the Agda proof assistant.
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 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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".