Using Traditional Knowledge to Adapt to Ecological Change: Denésoliné Monitoring of Caribou Movements
Bibliographic record
Abstract
The Chipewyan Dene or Denésoliné have long been dealing with variability in the movements of barren-ground caribou (Rangifer tarandus). Many generations ago, Denésoliné hunters learned that by observing caribou at key water crossings during the fall migration, they could obtain critical information about caribou health, population, and movement patterns. Systematic observation of these indicators by hunters strategically organized along the tree line enabled the Denésoliné to adapt their harvesting practices, including the location of family camps, to maximize harvest success. While this system of observation was developed for traditional subsistence harvesting, its techniques could be usefully applied today to other natural resource management contexts. In particular, such monitoring might help us understand how new bifurcation points created by mineral resource development may be affecting the Bathurst caribou herd. As governments, communities, and academics search for ways to include traditional knowledge in decision making for resource management, this paper recognizes that the Denésoliné and other indigenous peoples have their own systems of watching, listening, learning, understanding, and adapting to ecological change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".