Population Data BC: Supporting population data science in British Columbia.
Bibliographic record
Abstract
Background: Population Data BC (PopData) was established as a multi-university data and education resource to support training and education, data linkage, and access to individual level, de-identified data for research in a wide variety of areas including human and community development and well-being. Approach: A combination of deterministic and probabilistic linkage is conducted based on the quality and availability of identifiers for data linkage. PopData utilizes a harmonized data request and approval process 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 with a wide variety of tools for analysis. The SRE also allows for ongoing management and control of data. PopData continues to expand its data holdings and to evolve its services as well as governance and data access process. Discussion: PopData has provided efficient and cost-effective access to linked data sets for research. After two decades of learning, future planned developments for the organization include, but are not limited to, policies to facilitate programs of research, access to reusable datasets, evaluation and use of new data linkage techniques such as privacy preserving record linkage (PPRL). Conclusion: PopData 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 benefits of having a third-party data linkage and provisioning center for research purposes. Building further connections with existing data holders and governing bodies will be important to ensure ongoing access to data and changes in policy exist to facilitate access for researchers.
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.
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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.129 | 0.041 |
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".