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Record W2029165398 · doi:10.1145/1292597.1292604

The swiss coercion

2007· article· en· W2029165398 on OpenAlexaff
Stefan Monnier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCoercion (linguistics)Computer scienceProgrammerSoundnessProgramming languageContext (archaeology)Theoretical computer science

Abstract

fetched live from OpenAlex

Recent type systems allow the programmer to use types that describe more precisely the invariants on which the program relies. But in order to satisfy the type system, it often becomes necessary to help the type checker with extra annotations that justify why a piece of code is indeed well-formed. Such annotations take the form of term-level type manipulations, such as type abstractions, type applications, existential package packing and opening, as well as coercions, or casts. While those operations have no direct runtime cost, they tend to introduce extra runtime operations equivalent to n-redexes or even empty loops in order to get to the point where we can apply that supposedly free operation. We show a coercion that is like a pacific Swiss army knife of coercions: it cannot cut but it can instantiate, open, pack, abstract, analyze, or do any combination thereof, reducing the need for extra surrounding runtime operations. And all that, of course, for the price of a single coercion, which still costs absolutely nothing at runtime. This new coercion is derived from Karl Crary's coercion calculus [Crary, 2000], but can also replace Crary and Weirich's vcase [Crary and Weirich, 1999]. It additionally happens to come in handy to work around some limitations of value polymorphism. It is presented in the context of Shao et al.'s Type System for Certified Binaries [Shao et al., 2002]. Other than the coercion itself, another interesting aspect of this work is a slightly unusual proof technique to show soundness of the type erasure using a pure type assignment language, making the no-op nature of our cast more obvious.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.259
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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Same topicLogic, programming, and type systemsFrench-language works237,207