Splintered Hinterlands: Public Anthropology, Environmental Advocacy, and Indigenous Sovereignty
Bibliographic record
Abstract
This research analyzes the roles of action ethnobiology and public anthropology in “ecological distribution conflicts”—disputes over the benefits and burdens of natural resources—in policy-oriented research and advocacy. It considers the Natural Resources Defense Council's (NRDC) international campaigns to protect “frontier landscapes” of the Western hemisphere. Specifically, I examine case studies in Chilean Patagonia and the boreal forest region in the Canadian Northeast. Despite geographical, historical, and cultural differences, NRDC's campaigns in these two regions involved a shared focus on developing advocacy strategies that draw on biocultural knowledge to advance stronger environmental protections. NRDC and its local partners used ethnoecology as an environmental tactic to protect rivers from proposed large hydroelectric dam projects in Chile, and drew upon ethnozoology to preserve caribou threatened by industrial logging in Canada. To consider the synergies and tensions of environmental advocacy and Indigenous sovereignty in these two instances, I analyze partnerships between environmental activists, lawyers, and scientists on the one hand, and Indigenous leaders and local residents on the other. Taking a public anthropological approach, this comparative research sheds light on the role of action ethnobiology as a condition of possibility for advocacy to enhance environmental sustainability and Indigenous sovereignty across the Americas.
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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 | 0.001 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".