“Walk the Land, Before You Talk About the Land”: TEK‐Based Conservation Monitoring Invalidates Caribou Extirpation Status Assigned by British Columbia and Canada
Bibliographic record
Abstract
ABSTRACT The loss of species represents critical ecological events with far‐reaching implications for conservation biology. Accurate determinations of population status are therefore essential. Erroneous declarations of extinction or extirpation can lead to legal and policy inertia, the premature termination of recovery efforts, and the ongoing degradation of critical habitat. These outcomes ultimately heighten the risk to any remaining individuals and undermine Indigenous peoples’ cultural ways of life within which species are embedded. This study challenges the status designation of a caribou population with empirical evidence derived from a traditional ecological knowledge‐based conservation monitoring program initiated by West Moberly First Nations in the western subarctic of Canada. Relational, field‐based methods confirmed the presence of caribou where the governments of British Columbia and Canada had declared the species extirpated. These results necessitate an urgent reassessment not only of the status of the specific caribou subpopulation but also of broader conservation strategies, land use policies, and environmental monitoring. More fundamentally, the study underscores the imperative to center Indigenous knowledges in conservation biology and to critically examine the epistemic foundations that underpin species status determinations and recovery planning.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".