On Problems in Polymorphic Object-Oriented Languages With Self Types and Matching
Bibliographic record
Abstract
Subtyping is a basic concept in object-oriented languages. It supports subsumption but, unfortunately, it does not support inheritance of binary methods, i.e., methods taking another argument of type Self? – the same type as the object itself. For this reason, a relation, called matching, on recursive object types has been proposed. This relation does not support subsumption but it allows to inherit binary methods. Two different definitions of matching, called F-bounded and higher-order subtyping, have been proposed and discussed. It was shown that the higher-order interpretation has better theoretical properties, i.e., it leads to a reflexive and transitive matching relation. In this paper we concentrate on two problems in languages with self types and matching based on the higher-order interpretation. We show that the flexibility of self types may not allow the programmer to define certain classes and/or methods which are based on constant values. Furthermore, the higher-order interpretation, especially in the context of bounded quantification, is too restrictive. We argue that a language should be based on both versions of matching and a notion of a type This distinguished from the type Self.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.042 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| 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".