Indigenous protected and conserved areas (IPCAs): Canada's new path forward for biological and cultural conservation and Indigenous well-being
Bibliographic record
Abstract
Wilderness and national parks play a fundamental role in defining Canadian identity, yet Indigenous Peoples have historically been excluded from conservation decisions, resulting in systematic dispossession and oppression. In this article, we collaborate with Dene Tha'First Nation to discuss the recent paradigm shift towards Indigenous-led conservation and propose guiding principles to advance and assert the critical role of Indigenous Peoples in conservation. We begin with a brief history of Indigenous Peoples in conservation, followed by the concept of Indigenous protected and conserved areas (IPCAs). Our analyses show that IPCAs have gained momentum recently, driven by the Truth and Reconciliation Commission and Canada's commitment to global conservation goals. With one of the largest landmasses and Indigenous populations in the world, IPCAs in Canada have the potential to make immense contributions to environmental and cultural conservation rooted in an intrinsic relationship to the land. Despite this biocultural diversity, as of 2022, less than 1% of Canada's landmass is declared as Indigenous-led protected areas. However, more than 50 Indigenous communities across the country have currently received funding to establish IPCAs or to undertake early planning and engagement that could position Canada as a global leader in Indigenous-led conservation. As the Government of Canada aims to designate 25% of the territory as protected space by 2025 and 30% by 2030, embedding Indigenous rights, knowledge, and values in the national conservation strategy will be essential to simultaneously honoring the commitments to reconciliation and meeting the ambitious targets stipulated in the Kunming–Montreal Global Biodiversity Framework.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".