Affirming Indigenous data sovereignty in collaborative wildlife conservation in the era of open data
Bibliographic record
Abstract
Abstract In the current data‐driven landscape of wildlife conservation, data sovereignty (i.e. governance and security) is fundamental to determining how knowledge is created and applied to pressing biodiversity concerns. Western science increasingly champions open data, which uplifts data stewardship and sharing to make data more accessible. At the same time, Indigenous Peoples stress the importance of Indigenous data sovereignty (IDS) to assert Indigenous rights over Indigenous data. These two philosophies offer distinct approaches to data sovereignty, creating possible tensions in collaborations between Western scientists and Indigenous Peoples. Awareness of this issue in wildlife conservation is slowly growing, and practical guidance is needed for balancing IDS and open data in collaborations. Here, we review current literature to identify challenges in bringing IDS and open data together in collaborative wildlife conservation. We include specific commentary for wildlife sensors (camera traps and autonomous recording units) due to the growing use and demonstrated interest in open data practices for these technologies and their appeal in Indigenous‐led conservation. We describe three nested themes of data sovereignty: collaborative relationships, data governance and data stewardship. Within these themes, we identify nine recommendations to navigate potential IDS and open data tensions in collaborations, stressing the need to focus on relationship building, support Indigenous data governance agendas and to discuss upfront expectations for data stewardship. Additionally, we suggest reflective questions to consider throughout the data life cycle. This provides a framework for understanding the sociocultural implications of wildlife data and supporting IDS throughout the collaborative process, while identifying opportunities to apply open data stewardship practices. Synthesis and applications: Our work demonstrates the need for explicit recognition of IDS principles for wildlife data and proposes general guidance for addressing data sovereignty in collaborative initiatives. We recognize that our recommendations are not exhaustive and that each collaborative context brings unique challenges and opportunities. We invite continued discussion on data governance and stewardship to promote shared learning. Our recommendations can be applied more broadly to facilitate a new approach to data sovereignty in collaborations across the natural sciences. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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".