Growing together: A principle-based approach to building collaborative Indigenous partnerships in Canada’s forest sector
Bibliographic record
Abstract
While a great deal of recent research has focused on opportunities for Indigenous participation in Canada’s forest sector, relatively little has explored how to translate various lessons learned into inclusive and mutually-beneficial collaborative processes. Through a review of recent peer-reviewed literature examining Indigenous participation in forest management and development, this paper seeks to fill the current knowledge gap by proposing a set of five principles, with twentythree underlying supporting mechanisms, that can be adopted by Indigenous communities, resource managers and government policy makers to help facilitate meaningful collaboration within the forest sector. These principles include: building respectful relationships; broad community engagement; bridging knowledge and value systems; flexible and holistic management systems; and clear and relevant measures of success. Although the proposed principles may be implemented either individually or in various combinations, to both improve existing collaborative arrangements and develop new ones, they may be best conceptualized as an integrated, incremental process involving any number of motivated partners. It is hoped that the lessons presented in this article will serve as a basis for diverse stakeholder groups to better understand each other’s needs and ultimately work more effectively towards achieving respectful co-existence and equity in Canada’s forest sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".