Exploring Collaborative Frameworks to Assess and Monitor Conservation Outcomes of Indigenous Protected and Conserved Areas (IPCAs)
Bibliographic record
Abstract
Within Canada, active strives are being made to achieve Canada’s Target 1 conservation goal. The creation of area-based conservation methods such as Other Effective Conservation Measures (OECMs) and Indigenous Protected and Conserved Areas (IPCAs), provide the means to achieve these goals. However, the current screening tools used to identify and monitor OECMs and IPCAs heavily reflect exclusively western science, thereby creating barriers for Indigenous nations. This research uses the collaborative framework of Two- Eyed Seeing to identify potential criteria indicators that are inclusive of Indigenous traditional knowledge to assess the governance systems, cultural and spiritual outcomes, and conservation outcomes of IPCAs. A rapid literature review was conducted to analyze the current screening metrics used by the Canadian government which revealed the potential for criteria for monitoring metrics. This paper highlights the need for place-based conservation management, co-governance models and wellness indicators in current monitoring tools for OECMs and IPCAs.
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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 | 0.039 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".