Indigenizing the North American Model of Wildlife Conservation
Bibliographic record
Abstract
Although a diversity of approaches to wildlife management persists in Canada and the United States of America, the North American Model of Wildlife Conservation (NAM) is a prevailing model for state, provincial, and federal agencies. The success of the NAM is both celebrated and refuted amongst scholars, with most arguing that a more holistic approach is needed. Colonial rhetoric permeates each of the NAM’s constituent tenets—yet, beyond these cultural and historical problems are the NAM’s underlying conservation values. In many ways, these values share common ground with various Indigenous worldviews. For example, the idea of safeguarding wildlife for future generations, utilizing best available knowledge to solve problems, prioritizing collaboration between nations, and democratizing the process of conserving wildlife all overlap in the many ways that the NAM and common models of Indigenous-led conservation are operationalized. Working to identify shared visions and address necessary amendments of the NAM will advance reconciliation, both in the interest of nature and society. Here, we identify the gaps and linkages between the NAM and Indigenous-led conservation efforts across Canada. We impart a revised NAM—the Indigenizing North American Model of Wildlife Conservation (I-NAM)—that interweaves various Indigenous worldviews and conservation practice from across Canada. We emphasize that the I-NAM should be a continuous learning process that seeks to update and coexist with the NAM, but not replace Indigenous-led conservation.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".