Strategic approaches to Indigenous engagement in natural resource management: use of collaboration and conflict to expand negotiating space by three Indigenous nations in Quebec, Canada
Bibliographic record
Abstract
Indigenous peoples’ roles in Canadian forestry have expanded enormously during recent decades, encouraged by a variety of policies and programs from governments, industry, and Indigenous organizations. Many Indigenous communities have chosen to engage with non-Indigenous actors in multiple ways, both collaborative and conflictual, and this study investigates the extent to which these represent strategic choices. Through a study with the Atikamekw Nehirowisiwok, Huron-Wendat, and Mi’gmaq nations in Quebec, Canada, we examined processes used to present and promote their visions and objectives for their traditional forestlands, over more than 30 years. This analysis highlights the number and diversity of processes in which Indigenous peoples engage. Examining the series of processes and the roles of Indigenous participants in them allowed us to characterize the strategies adopted by each nation. This analysis helps us understand factors such as the roles of transformative and incremental change, the interactions between processes, and the importance of governing organizations. We conclude that rather than being stakeholders in state-sponsored initiatives, these Indigenous nations are constantly and actively using such processes as institutional and procedural spaces in which they are able to negotiate, promote, implement, and articulate multiple strategies that contribute to enhancing their role in forest and resource management.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".