Achieving the 30 by 30 Biodiversity Target in Canada through Indigenous Protected and Conserved Areas
Bibliographic record
Abstract
Canada has committed to the United Nations Kunming-Montreal Global Biodiversity Framework (GBF), including protecting 30% of land and sea by 2030 while recognizing Indigenous rights. By 2024, Canada has conserved 13.8% of terrestrial and 15.5% of marine protected and conserved areas (PCAs), leaving a major gap in protection. This article posits that Indigenous protected and conserved areas (IPCAs) are the best way to protect an additional 160 million hectares of land and 80 million hectares of sea required to meet GBF target 3 by 2030 and fulfill UNDRIP commitments, and reconciliation promises. We explore this potential by mapping IPCAs against governance, critical habitats for species at risk, peatlands, and greenstone belts. Currently, Indigenous governance is underrepresented; of nearly 15,000 PCAs, only 96 (Rangifer tarandus caribou) and other endangered species.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".