Meaningful Datatypes: Ontologically-Sound Dependent Type Systems for Data Science
Bibliographic record
Abstract
Data science incorporates many processes, techniques and domains to transform data into meaningful insights that inform decision-making. In practice, data science relies heavily on simplistic datatypes like strings or integers to represent common and significant complex real-world phenomena like time, mereology, and provenance. Current solutions to this problem are opaque, lack standardization, and require manual intervention to validate. To address these issues, we introduce the meaningful type safety framework (MeTS) to enforce the integrity of real-world interpretations via type-checking with dependent types. MeTS is also grounded in real-world census datasets from Statistics Canada, which also serve as the basis for a novel census data ontology and a theorem to prove the formal ontological soundness of a MeTS type environment. The MeTS framework, including its ontological foundations, challenges the status quo of data science, and introduces new avenues of research for the functional programming, data science, and knowledge modeling communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.023 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".