A method to map heterogeneity between near but non-equivalent semantic attributes in multiple health data registries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health registries from multiple jurisdictions often include terms that are assumed to be semantically equivalent (e.g. fetal death and stillbirth). Closer examination reveals that such attributes have near--but non-equivalent--semantics. Thus their degree of semantic heterogeneity is an important indicator of uncertainty associated with data integration between registries. We build an OWL-encoded ontology which formalizes the relationships between similar perinatal concepts found in different databases. We also introduce the concept of ontology-based metadata as a means of contextualizing such terms and linking context to the attribute data. This extended metadata are exported as XML from the health registries, and it--along with the OWL ontology--is interfaced via a web-based GUI accessible to health researchers. The GUI mapping serves as the basis for making ad hoc comparison and integration decisions. Uncertainty is addressed by precisely mapping semantic heterogeneity between fields.
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.
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 | 0.037 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it