Bibliographic record
Abstract
Generalized Algebraic DataTypes (GADTs) allow programmers to capture important invariants of their data structures through type annotations on data constructors. However when working with GADTs, it is often difficult to concisely and precisely express the way complex data manipulations maintain those invariants. One can approach the problem in a few different ways, given the arsenal of GHC’s current type extensions. One approach is to use type classes with functional dependencies. Another approach is to introduce yet more GADTs to capture the behavior of individual functions. A third alternative is to use type families, a recent introduction of GHC by which functions over types can be defined directly, much like term-level functions, and appear in type signatures. In this paper we illustrate the use of type families in the context of a typepreserving compiler written in Haskell. We compare the results with an ad-hoc, all-GADT solution. We argue that type families promote a more direct programming style that eliminates much code bloat and translates into increased run-time performance. They offer better modularity and require fewer type annotations than type classes, which require that class constraints be propagated between compilation phases. We also describe a use of type families to capture more complex data structure invariants. We mention current limitations that we face with these more advanced uses, in which we need to convince the type checker that the type families we define satisfy certain properties. We sketch a proposal of a language extension to directly support such properties. ?Universite de Montreal, C.P. 6128, succ. Centre-Ville, Montreal, Quebec, Canada. ++1 (514) 343-6111 x3545; {guillelj,monnier}@iro.umontreal.ca
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".