Ecosystem management and forestry planning in Labrador: how does Aboriginal involvement affect management plans?
Bibliographic record
Abstract
Aboriginal peoples are increasingly being invited to participate in sustainable forest management processes as a means of including their knowledge, values, and concerns. However, it is justifiable to ask if this participation does lead to changes in forest management plans and to outcomes in management activities. We review four forest management plans over 10 years (1999–2009) in Labrador, Canada, to determine if increasing involvement by the Aboriginal Innu Nation has led to changes in plan content. We also compare these plans with three plans from another forest management district where there is no Innu presence and with two provincial forest strategies . Analysis shows that Labrador plans prepared since 2000, when the Innu and the provincial government established a collaborative process, are different from all other plans reviewed. Four principal characteristics distinguish these plans: a structure based around ecological, cultural, and economic landscapes, a network of cultural and ecological protected areas, increased attention to social and cultural values, and greater emphasis on research and monitoring. This suggests that Innu involvement has in fact influenced the contents of these plans, developing an innovative approach to implementing ecosystem management and demonstrating the utility of involving Aboriginal peoples in forest management planning processes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".