Indigenous-led conservation in the Arctic supports global conservation practices
Bibliographic record
Abstract
Amid growing recognition for the role of global conservation initiatives in protecting biodiversity and mitigating climate change impacts, the interest in Indigenous-led and Indigenous-centered conservation in the circumpolar Arctic is also on the rise. Through literature and practice, Indigenous communities in the Arctic are shaping the global discourse around conservation approaches, mechanisms, and strategies, and are challenging colonial conceptions of how lands, waters, and species should be used, managed, and protected. Indigenous approaches, mechanisms, and strategies often differ from those found in the global conservation toolbox and rather focus on local priorities, Indigenous knowledge, traditional practices, sovereignty, and self-determination. Direction on how conservation should evolve and overcome challenges and related burdens is best given by Indigenous communities, scholars, organizations, and governments. Valuing Indigenous knowledge and supporting community-level initiatives, strategies, and practices comes with the benefits of understanding, forwarding, and implementing community priorities, needs, and values through attention and focus on funding, Indigenous-led research and management, and mutual mentorship. In addition to benefiting conservation itself, biodiversity research conducted within Indigenous homelands has the opportunity to serve as a model for how regional, national, and international initiatives best engage with Indigenous knowledge, conservation practice, and policy development in the Arctic and beyond.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".