The Integration of Indigenous Knowledge in Canadian Protected Areas to Foster Conservation, Reconciliation, and Tourism Development
Bibliographic record
Abstract
For many Indigenous communities across Canada, the histories of tourism in parks are filled with complex experiences of displacement and cultural loss. The nation’s first protected area, Banff National Park (BNP), plays a central role in the imaginary of what many Canadians believe parks should represent: beautiful landscapes and wilderness. This romantic notion of Banff erases the traumatic legacies around the formation of the national park system, early tourism development in Western Canada, and the serious impacts it had on local Indigenous communities. Similar consequences were experienced by diverse Indigenous communities across the country where parks and tourism intersected with Indigenous ancestral lands. However, colonial practices of park management are being replaced by consultation processes that favor Indigenous management frameworks. Preceded by ground-breaking legislation that supports Indigenous land rights, the shifting dynamics of colonial power have led to new designations of parks Indigenous Protected and Conserved Areas (IPCAs) and changing management practices in established parks. Guided by Indigenous methodologies (IM) and based on semi-structured interviews with Indigenous Elders, leaders, and knowledgeable land users, this research examined the following key questions: How have colonial governments impacted Indigenous communities through tourism development in Canadian parks?; What is the future for Indigenous conservation models and tourism facilitated by new park designations?; How can tourism in parks be a key facilitator of reconciliation and decolonization processes in Canada. It is argued that Indigenous-led conservation practices in parks have the potential to support healthy ecosystems, regional economies, and the preservation of cultural values
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".