Cross-jurisdictional data access processes and coordination in two countries: key learnings and innovative approaches.
Bibliographic record
Abstract
ObjectivesAccess to cross-jurisdictional data can be beneficial to researchers but challenges exist in accessing data from different legal jurisdictions and policy environments. Two national networks in Australia and Canada will share their respective approaches to supporting the discovery of, and coordinating applications and approvals for, cross-jurisdictional data access.
 ApproachCollaboration between these two networks in the last year have allowed members to gain a better understanding of the approaches that have been put in place to support the coordination of cross-jurisdictional data applications. Through regular inter-network discussion and joint workshops, we have gained insight into the similarities, differences and challenges faced by each and identified common goals for continued shared learnings.
 ResultsSimilarities and differences exist between the approach and processes each network has implemented to coordinate applications for cross-jurisdictional data. In both countries, a single point of contact model has been key in coordinating data requests centrally. Whereas in one country, virtual access to linked data is provided centrally, different legal and policy environments across jurisdictions in another country are barriers to accessing data in one location. Each network has developed processes and tools to meet researcher needs while respecting local requirements and are turning to innovative strategies. For example, one network is developing infrastructure to support analysis of data in a distributed way, and both groups are creating centralized tools to streamline the data discovery and access processes to improve researcher experience.
 ConclusionThe coordination of applications for cross-jurisdictional data access by two national networks has streamlined researcher access, but challenges exist to fully meet researcher needs for cross-jurisdictional analyses. The continued cross-network collaboration will allow for shared learning opportunities and development of innovative solutions to tackle challenges related to cross-jurisdictional data access.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".