Integrating siloed data: A methodological approach to housing research in Ottawa, Canada
Bibliographic record
Abstract
Siloed data hinder the development of meaningful data tools on social and health issues. Housing is a social determinant of health that in recent years has become a major affordability issue in Canada. Data tools that provide a comprehensive overview of housing are necessary to support evidence-based policy, but housing data in Canada are siloed within a disparate array of data stewards. We describe the process of identifying and acquiring housing datasets from a wide variety of sources to create an integrated housing profile for Ottawa, Canada using a natural neighbourhood construct. We disseminated this knowledge through interactive maps and storytelling narratives. We offer recommendations to facilitate research using secondary data from multiple sources, including developing professional networks for inter-organizational collaboration, standardizing meta-data across data stewards, and using creative narratives to integrate data in dissemination.
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.032 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".