Aboriginal forestry: development of a socioecologically relevant moose habitat management process using local Cree and scientific knowledge in<i>Eeyou Istchee</i>
Bibliographic record
Abstract
Sustainable management of natural ecosystems requires adequate participation of Aboriginal people. This especially includes the joint use of local ecological and scientific knowledge to document natural processes and develop management guidelines. Despite increasing recognition of this principle, endorsed by the international community and several Aboriginal nations, there are very few genuine cases that show significant progress in this discipline. This case is similar in North American forestry where several initiatives have documented Aboriginal land use without ever significantly recognizing local knowledge in the development of forest management guidelines. In the search for innovative solutions on this topic, the Waswanipi Cree Model Forest developed a governance tool that allows Cree land users to translate their needs into management plans. We collaborated on this initiative by developing and testing a participatory approach, allowing the development of moose ( Alces alces L.) habitat management guidelines, better adapted to the socioecological context of the Cree. This innovative approach increases mutual understanding between Aboriginal and non-Aboriginal managers and favours the social acceptability of the guidelines while contributing to a more sustainable management of this northern and fragile ecosystem. This study will influence stakeholders striving to improve collaborative ecosystem management with Aboriginal people.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".