Traditional ecological knowledge and natural resource management
Bibliographic record
Abstract
Introduction (Charles R. Menzies - University of British Columbia and Caroline Butler -University of Northern British Columbia)Part 1: Indigenous Practices and Natural Resources1: Tidal Pulse Fishing: Selective Traditional Tlingit Salmon Fishing Techniques on the West Coast of the Prince of Wales Archipelago (Steve J. Langdon - University of Alaska-Anchorage) 2: As it was in the Past: A Return to the Use of Live Capture Technology in the Aboriginal Riverine Fishery (Kimberly Linkous Brown) 3: The Forest and the Seaweed: Gitga'at Seaweed, Traditional Ecological Knowledge and Community Survival (Nancy J. Turner - University of Victoria and Helen Clifton - Elder of the Gitga'at Nation) 4: Ecological Knowledge, Subsistence, and Livelihood Practices: The Case of the Pine Mushroom Harvest in Northwestern British Columbia (Charles R. Menzies)Part 2: Local Knowledge and Contemporary Resource Management5: Historizing Indigenous Knowledge: Practical and Political Issues (Caroline Butler) 6: The Case of the Missing Sheep: Time, Space, and the Politics of Trust in Co-Management Practice (Paul Nadasdy - University of Wisconsin-Madison) 7: Local Knowledge, Multiple Livelihoods, and the Use of Natural and Social Resources in North Carolina. (David Griffith - East Carolina University) 8: Integrating Fishers' Knowledge into Fisheries Science and Management: Possibilities, Prospects, and Problems (James R. McGoodwin - University of Colorado-Boulder)Part 3: Learning from Local Ecological Knowledge: Practical Approaches9: Honoring Aboriginal Science Knowledge and Wisdom in an Environmental Education Graduate Program (Gloria Snively - University of Victoria) 10: Traditional Wisdom as Practiced and Transmitted in Northwestern British Columbia, Canada (John Corsiglia - University of Victoria)Afterword: Making Connections for the Future (Charles R. Menzies)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".