Modular Formal Islands: Embed theory in your practice
Bibliographic record
Abstract
Motivated by the proliferation and usefulness of Domain Specific Languages as well as the demand in enriching well established languages by high level capabilities like modularity, pattern matching or strategic rewriting, we have introduced in previous works the Formal Islands framework. The main idea consists in integrating, in existing programs, formally defined parts called Islands, on which proofs and tests can be meaningfully developed. Then, Formal Islands could be safely dissolved into their hosting language to be transparently integrated in the existing user environment. We present this generic framework and we show that language extensions like Mhtml—providing modular constructions for html— or Tom—a Java language extension allowing for pattern matching and rewriting—are indeed Islands and can therefore be used to embed formal software developments into legacy code.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".