Oceanic Environmental and Indigenous Data: Reconciliation of Indigenous Data Governance and Sovereignty within Environmental Data Repositories
Bibliographic record
Abstract
Ocean Networks Canada (ONC) straddles the nexus between community relations, infrastructure installations, and data storage. As such, ONC has opportunities to partner with Indigenous communities and incorporate CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) from the point of engagement through to implementation and curation of Indigenous data. To further our commitment to CARE, ONC is implementing Local Contexts label compatibility into our metadata and dataset architecture. Local Contexts can help Indigenous communities reassert their cultural authority to dictate to users how their own data are to be collected, managed, displayed, accessed, and used (Local Contexts, 2024) while hosted on ONC’s data repository. Data agreements are binding agreements between ONC and our various partners – including Indigenous communities – to establish conditions and rules for our continued collaborations. Sensitive datasets can be restricted at the discretion of the community, and a pathway exists in which data users can make requests if they wish to pursue access to the restricted data. ONC as a custodian and distributor does not hesitate to relinquish ownership of data to further promote Indigenous data sovereignty and governance. A stake in ownership of community-based monitoring data is not a necessity for ONC but is rather a discussion to have with our Indigenous partners. License and Policy attribution to the data can be decided during negotiation of the data agreement between parties, where we can then apply Creative Commons (2024) Licenses or other licenses as deemed fit by the community. These processes are set during the consultation phase, however. Indigenous communities are dynamic, and their needs can change over time. What happens if a community wishes to change one of these previously agreed-upon rules? Local Context Labels are one way for Indigenous communities to flex their authority to control the data which they own. When a community changes their project labels, it will get picked up in ONC’s dataset metadata and landing pages. In practice, this is one method communities can leverage to prompt new amendments of agreements by ONC. These labels can also be used by communities to express traditional heritage, biocultural phenomena, or other notices of significance directly to the end-user. In a pilot project, ONC will develop the software architecture to support Local Context labelling. Using guiding documents and best practices, ONC will implement this in our underlying metadata profiles such DataCite Fabrica and ISO19115. Further software development will make labels visible in our Dataset Landing Pages (DOIs). The labels will be inherited directly from the Local Contexts application programming interface (API) of projects associated with our Indigenous partner communities. Since there are currently no partner communities using Local Contexts, ONC will also design a “mock community” with ONC-owned data to provide tangible examples of Local Contexts dataset integration to our partners and the broader scientific community. Should the pilot project be deemed successful, ONC will then fully implement Local Contexts compatibility with our production version of ONC’s Oceans 3.0 Data Portal. Communities ONC engages with will then have the option to utilize Local Contexts to further their own Indigenous data governance and sovereignty.
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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 | 0.100 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.029 | 0.037 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".