Bibliographic record
Abstract
Indexed data types allow us to specify and verify many interesting invariants about finite data in a general purpose programming language. In this paper we investigate the dual idea: indexed codata types, which allow us to describe data-dependencies about infinite data structures. Unlike finite data which is defined by constructors, we define infinite data by observations. Dual to pattern matching on indexed data which may refine the type indices, we define copattern matching on indexed codata where type indices guard observations we can make. Our key technical contributions are three-fold: first, we extend Levy's call-by-push value language with support for indexed (co)data and deep (co)pattern matching; second, we provide a clean foundation for dependent (co)pattern matching using equality constraints; third, we describe a small-step semantics using a continuation-based abstract machine, define coverage for indexed (co)patterns, and prove type safety. This is an important step towards building a foundation where (co)data type definitions and dependent types can coexist.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".