Newly designated Indigenous Protected and Conserved Areas in Canada’s North : another label for inequitable co-management agreements or an honest attempt to walk the road of reconciliation?
Bibliographic record
Abstract
Inclusion of Indigenous communities and Traditional Ecological Knowledges (TEK) alongside reconciliation efforts feature in numerous plans and policies for nature and biodiversity conservation. But to what extent do these agreements present an honest attempt to equally share power and responsibility between Indigenous peoples and governance agencies in protected area management? In this thesis, I trace how including Indigenous communities and their TEK entered Canada’s policy discourse on nature conservation. I focus on the designation of Indigenous Protected and Conserved Areas (IPCAs), which presents Canada’s latest approach towards including Indigenous peoples in protected area management. Through a study of policy documents, I compare changes in Canadian governance agencies’ proposal of and motivations behind Indigenous peoples’ inclusion with insights from Indigenous communities’ documents related to Edéhzíe Protected Area and Thaidene Nëné Indigenous Protected Area. These documents offer insights into Indigenous stewardship practices, emphasize Indigenous self-governance as well as the role of TEK, Western science, and Indigenous languages in IPCA management. Although I conclude that Edéhzíe Protected Area and Thaidene Nëné Indigenous Protected Area present an honest attempt to equally share power and responsibility in IPCA management, I call on governance agencies to further centre Indigenous peoples’ ideas on stewarding biodiversity-rich places, grant rights to self-determination and self-governance, and restore justice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".