Traditional ecological knowledge in Northwestern BC parks: Exploring paths to reconciliation and self-determination in park planning and operations management
Bibliographic record
Abstract
,This research explores how Traditional Ecological Knowledge (TEK) can be meaningfully integrated into BC Parks’ planning and operations management to enhance socially and environmentally responsible management plans. The objectives of this study were to identify historical and current socio-political barriers to the inclusion of TEK in park planning and operations management and to develop recommendations for park planners and managers to integrate TEK that prioritize reconciliation and self-determination. There is a paucity of research examining the inclusion of TEK and the roles of reconciliation and self-determination in park planning and management at the provincial level in Canada. Qualitative semi-structured interviews with Gitxsan First Nation Chiefs and Elders and BC Parks North Coast Skeena regional staff were analyzed to develop practical recommendations for the inclusion of Indigenous Peoples and their Knowledges in BC Parks planning and operations management. These recommendations address relations of power and policies, and they prioritize reconciliation and self-determination as a strategy for social change. I argue that the inclusion of TEK is necessary to improve park planning and management and to address the larger social and environmental issues in society. The findings of this study contribute empirical evidence to ongoing academic discussions regarding Indigenous inclusion, TEK, reconciliation, and selfdetermination in park planning and management. This work responds to the federal government’s Truth and Reconciliation Commission’s Calls to Action and British Columbia’s Declaration on the Rights of Indigenous Peoples Act, and it is my hope that this project contributes to advancing reconciliation and self-determination in park planning and management.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| 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".