"Put on Your Caribou Hat": Challenges to and Strategies for Successful Co-Stewardship of North American Caribou Herds
Bibliographic record
Abstract
approaches to stewardship of the environment improve management outcomes by diversifying the perspectives, knowledge, and data that feed into decisions, and by sharing the power and responsibility of the management process.Our profile article provides a summary of a panel discussion by Indigenous leaders on the topic of co-stewardship of caribou held at the most recent joint meeting of the North American Caribou Workshop and Arctic Ungulate Conference, which was held in Anchorage, Alaska, 8-12 May 2023.It is our hope that this summary will allow others involved in co-stewardship efforts to learn from the experiences of these panellists, and help Indigenous communities, management agencies, and researchers better understand factors that contribute to the success of co-management efforts.The theme of the conference was Crossing Boundaries, and our panel discussion, titled Caribou Crossing: Collaborative Caribou Stewardship in a Changing Arctic, was focused on collaborative management of barren-ground caribou (Rangifer tarandus).The panel brought together leaders from Alaska and northwestern Canada who have been at the forefront of co-management organizations, including the Western Arctic Caribou Herd Working Group (WACH WG), the Porcupine Caribou Management Board (PCMB), the Beverly and Qamanirjuaq Caribou Management Board (BQCMB), the Ahtna Intertribal Resource Commission (AITRC), and the Advisory Committee for Cooperation on Wildlife Management (ACCWM).Co-management organizations typically include public and Indigenous organizations, caribou harvesters, and wildlife biologists and managers working together.The Caribou Crossing panel discussion focused on strategies that have helped co-management organizations overcome challenges to conservation, support continuation of subsistence uses, and include people who rely on barrenground caribou in management decisions, while also noting the obstacles that remain.In this article, we-the panellists, moderator, and organizers-share the outcome
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".