Population Data BC: Supporting population data science in British Columbia
Bibliographic record
Abstract
BackgroundPopulation Data BC (PopData) was established as a multi-university data and education resourceto support training and education, data linkage, and access to individual level, de-identified data forresearch in a wide variety of areas including human and community development and well-being. ApproachA combination of deterministic and probabilistic linkage is conducted based on the quality andavailability of identifiers for data linkage. PopData utilizes a harmonized data request and approvalprocess for data stewards and researchers to increase efficiency and ease of access to linked data.Researchers access linked data through a secure research environment (SRE) that is equipped witha wide variety of tools for analysis. The SRE also allows for ongoing management and control ofdata. PopData continues to expand its data holdings and to evolve its services as well as governanceand data access process. DiscussionPopData has provided efficient and cost-effective access to linked data sets for research. After twodecades of learning, future planned developments for the organization include, but are not limitedto, policies to facilitate programs of research, access to reusable datasets, evaluation and use of newdata linkage techniques such as privacy preserving record linkage (PPRL). ConclusionPopData continues to maintain and grow the number and type of data holdings available for research.Its existing models support a number of large-scale research projects and demonstrate the benefitsof having a third-party data linkage and provisioning center for research purposes. Building furtherconnections with existing data holders and governing bodies will be important to ensure ongoingaccess to data and changes in policy exist to facilitate access for researchers.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".