Conceptualizing community data governance for race-related, population data: a scoping review and key informant interviews
Bibliographic record
Abstract
ObjectiveThere is growing recognition of the importance of community data governance to build accountability of research institutions to communities. Our organization, a steward of health and administrative population-level data, has previously implemented community governance structures for Indigenous data. This scoping initiative explores development and implementation of an additional community governance structure for race-related data. ApproachWe conducted a scoping review of peer-reviewed and grey literature to identify existing practices of community data governance. We also conducted key informant interviews with thirteen racialized community stakeholders, who addressed open-ended questions on potential co-design processes as well as governance mandates, scopes, barriers, and facilitators. ResultsThe scoping review identified eight community data governance examples. Two of these pertained to race-related data, while the remaining six pertained to other data that identified “community” geographically, by disease condition, life stage, and/or economic circumstance. Governance structures were diverse, ranging from one-time crowd-design of a data-sharing agreement to quarterly meetings of a governing board to review project-level data requests. Key informant interviews provided four themes to guide implementation in the context of our organization: exploring organizational readiness, considering who should be involved, defining the scope and mandate, and drafting an approach and process. ConclusionWe are committed to implementing a community governance structure for race-related, population-level data. However, there are limited examples of similar structures in the existing literature. ImplicationsThe identified examples and the advice of community stakeholders will guide co-design of a preliminary structure, scope, and mandate for community governance of race-related, population-level data.
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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 | 0.046 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.019 |
| Open science | 0.027 | 0.020 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".