Taking care of knowledge, taking care of salmon: towards Indigenous data sovereignty in an era of climate change and cumulative effects
Bibliographic record
Abstract
In this paper, we argue that Indigenous data sovereignty (IDS) is vital for addressing threats to ecosystems, as well as for Indigenous Peoples re-establishing and maintaining sovereignty over their territories. Indigenous knowledge-holders face pressure from non-Indigenous scientists to collaborate to address environmental problems, while the open data movement is pressuring them to make their data public. We examine the role of IDS in the context of cumulative effects and climate change that threaten salmon-bearing ecosystems in British Columbia, guided by content from an online workshop in June 2022 and attended exclusively by a Tier-1 audience (First Nations knowledge-holders and/or technical staff working for Nations). Attention to data is required for fruitful collaborations between Indigenous communities and non-Indigenous researchers to address the impacts of climate change and the cumulative effects affecting salmon-bearing watersheds in BC. In addition, we provide steps that Indigenous governments can take to assert sovereignty over data, recommendations that external researchers can use to ensure they respect IDS, and questions that external researchers and Indigenous partners can discuss to guide decision-making about data management. Finally, we reflect on what we learned during the process of co-creating materials.
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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 | 0.166 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.046 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".