Fixpoints for the masses: programming with first-class Datalog constraints
Bibliographic record
Abstract
Datalog is a declarative logic programming language that has been used in a variety of applications, including big-data analytics, language processing, networking and distributed systems, and program analysis. In this paper, we propose first-class Datalog constraints as a mechanism to construct, compose, and solve Datalog programs at run time. The benefits are twofold: We gain the full power of a functional programming language to operate on Datalog constraints-as-values, while simultaneously we can use Datalog where it really shines: to declaratively express and solve fixpoint problems. We present an extension of the lambda calculus with first-class Datalog constraints, including its semantics and a type system with row polymorphism based on Hindley-Milner. We prove soundness of the type system and implement it as an extension of the Flix programming language.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".