Empowering Indigenous Communities in Data Governance Through Local Contexts
Bibliographic record
Abstract
Ocean Networks Canada (ONC) recognizes the critical role of Indigenous communities in shaping data governance practices that reflect their unique cultural, ethical, and environmental priorities.To support Indigenous data sovereignty, ONC is integrating Local Contexts labels functionality into its metadata profiles and dataset infrastructure. Local Contexts enables Indigenous communities to assert control over how their data is collected, accessed, and used while hosted on ONC’s data repository; fostering transparency and ensuring that cultural significance is preserved. By embedding Local Contexts information, communities can apply labels of biocultural heritage, traditional knowledge, or other culturally specific information directly to datasets. This empowers them to restrict access to sensitive data and communicate usage guidelines to users in a manner aligned with their community’s values. Communities retain the flexibility to update these labels as their governance needs evolve, ensuring that their authority remains central to the data lifecycle.ONC is implementing this functionality using DataCite and ISO 19115 to ensure metadata is both machine- and human-readable. ONC’s pilot project will demonstrate Local Contexts labels integration through designing a “mock community” with ONC-owned data, providing Indigenous partners with tangible examples of how these tools can enhance their data governance and sovereignty. This initiative is designed to align with the CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) by prioritizing the individual needs and perspectives of Indigenous communities.Ultimately, the integration of Local Contexts labels into ONC’s data repository offers a scalable model to further promote Indigenous data sovereignty and governance. This ensures that communities remain the primary stewards of their cultural and environmental knowledge.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".