Beyond a Mapping Exercise: Inclusion of Aboriginal Traditional Ecological Knowledge in Parks and Protected Areas Management
Bibliographic record
Abstract
This paper examines current approaches for Parks and Protected Areas (PPA) managers in incorporating Aboriginal Traditional and Ecological Knowledge (ATEK) into their management plans. This paper focuses on two case-studies. They are Nahanni National Park and Reserve in the Dehcho region of the Northwest Territories, and the Whitefeather Forest Protected Area in the Pikangikum First Nations Traditional Territory in Ontario. They were chosen because of their unique approaches to include Aboriginal communities in the planning process and their designation as UNESCO World Heritage sites. The broader indigenous involvement policies of both Parks Canada and Ontario Parks are examined using academic literature review and a document-based case study from each agency. The paper sets out to understand where potential disconnects have occurred and if there are any tools to be used to utilize ATEK in the implementation of cooperative management plans focusing on PPA management. The question is asked: Are there any areas where planners can work in a more meaningful manner with Aboriginal communities to utilize the depth of knowledge that to date has remained largely underutilised? Most fundamentally, for current federal and provincial parks and protected areas management to include Aboriginal Traditional and Ecological Knowledge, and create a positive cooperative management method, there needs to be a fundamental shift in policies. Foremost is the building of the relationship of Aboriginal communities and Crown Agency. They must seek to braid ATEK and Western Science, to balance knowledge, include Aboriginal voice in a meaningful and substantive manner. More practically, this review suggests the government agencies need to make fundamental changes in their policies to ensure the inclusion of Aboriginal Traditional Ecological Knowledge in Parks and protected areas management is standardised across the province of Ontario and Canada.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| 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".