Traditional Ecological Knowledge and Natural Resource Management
Bibliographic record
Abstract
Traditional Ecological Knowledge and Natural Resource Management examines how traditional ecological knowledge (TEK) is taught and practiced today among Native communities. Of special interest is the complex relationship between indigenous ecological practices and other ways of interacting with the environment, particularly regional and national programs of natural resource management. Focusing primarily on the northwest coast of North America, scholars look at the challenges and opportunities confronting the local practice of indigenous ecological knowledge in a range of communities, including the Tsimshian, the Nisga’a, the Tlingit, the Gitksan, the Kwagult, the Sto:lo, and the northern Dene in the Yukon. The experts consider how traditional knowledge is taught and learned and address the cultural importance of different subsistence practices using natural elements such as seaweed (Gitga’a), pine mushrooms (Tsimshian), and salmon (Tlingit). Several contributors discuss the extent to which national and regional programs of resource management need to include models of TEK in their planning and execution. This volume highlights the different ways of seeing and engaging with the natural world and underscores the need to acknowledge and honor the ways that indigenous peoples have done so for generations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".