Achieving the 30 by 30 Biodiversity Target in Canada Through Indigenous Protected and Conserved Areas by Recognizing Indigenous Rights
Bibliographic record
Abstract
Canada’s commitment to the United Nations Kunming-Montreal Global Biodiversity Framework (GBF)’s target three is protecting 30% of land and waters by 2030. In 2024, only 13.7% of terrestrial and 14.7% of marine areas are protected and conserved areas (PCAs), necessitating an additional 1.6 million square kilometers (sq km) of land and 0.8 million sq km of ocean. Our GIS-based research indicates Indigenous protected and conserved areas (IPCAs) hold significant potential to bridge this gap. Currently, four Indigenous-governed IPCAs protect only 0.05% of land in Canada. These IPCAs are on modern treaty lands. Canada’s vast peatlands are excluded from PCAs, despite the peatlands’ ecological integrity, abundance of Indigenous food sources, and importance for climate change, due to mining interests on greenstone belts. This requires Indigenous-led governance of IPCAs, independent of the Crown's control, to qualify for the GBF target, advance reconciliation, and implement the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). By recognizing Indigenous self-determination and self-government, protecting half of all peatlands alongside all proposed IPCAs would enable Canada to achieve its 30 by 30 GBF target and safeguard critical habitats.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".