Indigenous-driven conservation: exploring the planning of Qikiqtait protected area in Sanikiluaq, Nunavut
Bibliographic record
Abstract
Biodiversity loss is increasing worldwide due to anthropogenic pressures. Protected areas are viewed as a primary tool to prevent biodiversity loss. However, protected areas do not always meet local needs and biodiversity goals simultaneously. Increasingly, local, and Indigenous communities globally are initiating protected areas that better reflect local needs while at the same time meeting conservation objectives. The purpose of this thesis is to examine how an Indigenous, community-driven approach to protected area planning differs from the model more typically used by conservation and government agencies in Canada. Using literature and examples of community-based protected areas in Canada, this research sought to synthesize the current conservation framework. Focusing on the Belcher Islands, Nunavut, this research examines the proposed protected area of Qikiqtait, a community-based, Indigenous-led protected area initiated by the community of Sanikiluaq, Nunavut. Geographic Information Systems (GIS) spatially compare Priority Areas for Conservation (PACs) derived from two different approaches: the community of Sanikiluaq, and World Wildlife Fund Canada (WWF). The analyses indicated a large overlap between areas of importance for the community and areas of conservation importance for the region identified by the WWF. Overall, the community planning offers a finer spatial resolution more suitable to local planning, as well as encompassing a broader range of conservation and livelihood priorities. Following the literature review and spatial analysis, this research concludes that while Canada’s conservation framework is increasingly making space for greater Indigenous leadership and participation, lessons remain on how to achieve optimum potential in community-based protected areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".