Using local ecological knowledge as evidence to guide management: A community‐led harvest calculator for muskoxen in Greenland
Bibliographic record
Abstract
Abstract Indigenous people manage or have tenure rights on over a quarter of the world's land surface. While there is growing interest in “evidence‐based” natural resource management, there are few documented experiences with “evidence‐based” practice in community‐managed lands. We explore the evidence required for decisions about harvesting of a community‐managed muskox herd in Greenland, and the collaboration needed to acquire this evidence. We present the development, application, and outcome of a user‐friendly demographic model—a harvest calculator—and we show how Local Ecological Knowledge was used throughout the process and combined with scientific knowledge. The community members identified suitable harvest scenarios with the use of the calculator. The calculator's predictions corresponded with their own perceptions of declining numbers of muskox bulls and suggested that reversal was possible under an alternative harvest scenario. As a result, the community members used the findings to request a revised muskox harvest quota, which gained immediate approval by the government. We draw on our experience to propose where community‐led harvest calculators can be useful. Community‐led harvest calculators can help indigenous and local communities develop economically within environmentally sustainable limits, while at the same time providing community members a “voice” in natural resource governance. An effective local management regime will require the sustained application of this tool.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".