Biodiversity, traditional management systems, and cultural landscapes: examples from the boreal forest of Canada
Bibliographic record
Abstract
There is a relationship between biodiversity conservation and the cultural practices of indigenous and traditional peoples regarding land and resource use. To conserve biodiversity we need to understand how these cultures interact with landscapes and shape them in ways that contribute to the continued renewal of ecosystems. This article examines the significance of traditional knowledge and management systems and their implications for biodiversity conservation. We start by introducing one key traditional ecological practice, succession management, in particular through the use of fire. We then turn to the example of the indigenous use of boreal forest ecosystems of northern Canada, with a focus on the Anishnaabe (Ojibwa) of north‐western Ontario. Their traditional practices and cultural landscapes provide temporal and spatial biodiversity, and examples of the mechanisms that conserve biodiversity. Learning from traditional systems is important for broadening conservation objectives that can accommodate the sustainable livelihoods of local people. The lens of cultural landscapes provides a mechanism to understand how multiple objectives (timber production, non‐timber forest products, protected areas, tourism) are central to sustainable forest management in landscapes that conserve heritage values and support the livelihood needs of local people. The use of broader and more inclusive definitions of conservation and multiple, integrated objectives can help reconcile local livelihood needs and biodiversity conservation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".