Qualified Types with Boolean Algebras
Bibliographic record
Abstract
We propose type qualifiers based on Boolean algebras. Traditional type systems with type qualifiers have been based on lattices, but lattices lack the ability to express exclusion . We argue that Boolean algebras, which permit exclusion, are a practical and useful choice of domain for qualifiers. In this paper, we present a calculus System F <:B that extends System F <: with type qualifiers over Boolean algebras and has support for negation, qualifier polymorphism, and subqualification. We illustrate how System F <:B can be used as a design recipe for a type and effect system, System F <:BE , with effect polymorphism, subeffecting, and polymorphic effect exclusion. We use System F <:BE to establish formal foundations of the type and effect system of the Flix programming language. We also pinpoint and implement a practical form of subeffecting: abstraction-site subeffecting. Experimental results show that abstraction-site subeffecting allows us to eliminate all effect upcasts present in the current Flix Standard Library.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".