Boreal forest prospects and politics: Paradoxes of first nations participation in multi-sector conservation
Bibliographic record
Abstract
This article explores the prospects and politics of indigenous participation in multi-sector conservation—an integrative and proactive new approach to sustaining the integrity of vast natural ecosystems—by presenting the case of the Boreal Leadership Council (BLC), an initiative comprised of Environmental Non-Governmental Organisations (ENGOs), First Nations groups, resource-extractive corporations, and financial institutions committed to collectively addressing issues impacting Canada's boreal forest. Drawing on multi-sited participant-observation and interviews with BLC members and affiliates, I show how the BLC challenges wilderness-oriented definitions of conservation by undertaking projects that intertwine resource use, land rights, cultural preservation, and political authority, but concurrently perpetuates dominant perspectives by adhering to discursive practices that limit how environmental information can be persuasively presented. Ultimately, I argue that multi-sector conservation creates both new possibilities for indigenous empowerment and new forms of marginalisation through the reproduction of a (post)colonial geography of exclusion in which indigenous participants knowingly and strategically travel from the centre of their own worlds to peripheral positions within a larger—and inherently inequitable—sociopolitical structure.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.037 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".