Letting the Leaders Pass: Barriers to Using Traditional Ecological Knowledge in Co-management as the Basis of Formal Hunting Regulations
Bibliographic record
Abstract
"We studied a case of failure in applying traditional ecological knowledge (TEK) in comanagement as the basis for formal hunting regulations. We based the study on the Porcupine Caribou (Rangifer tarandus) Herd 'let the leaders pass' policy, established for the Dempster Highway of the Western Canadian Arctic, and identified conditions creating barriers in the successful application of TEK through comanagement. Stated as propositions, identified barriers include: (1) the context-specific nature of TEK limits its application in resource management regulations; (2) changes in traditional authority systems, hunting technology, and the social organization of harvesting caribou affect the effectiveness of TEK approaches in a contemporary social setting; (3) indigenous efforts toward self-government and political autonomy limit regional comanagement consensus in a heterogeneous cultural landscape; (4) the mismatch of agency enforcement of hunting regulations and TEK-based education is problematic. We analyzed the case through four historical phases of caribou management, complementing the study with a literature review of TEK and wildlife comanagement to explain why TEK integration of caribou leaders in regulatory resource management fell short of success. Identifying and understanding the social dynamics related to these barriers make apparent solutions for transforming the comanagement process."
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".