“Borders don’t protect areas, people do”: insights from the development of an Indigenous Protected and Conserved Area in Kitasoo/Xai’xais Nation Territory
Bibliographic record
Abstract
Indigenous Protected and Conserved Areas (IPCAs) have gained global attention because of renewed interest in protecting biodiversity during a time of Indigenous resurgence. However, few examples in academic literature illustrate Indigenous Peoples’ rationale and processes for developing IPCAs. This paper fills that gap, describing a participatory action research collaboration with the Kitasoo/Xai’xais Nation. We used document analysis, interviews, and community engagement to summarize the Nation’s perspectives while assisting Kitasoo/Xai’xais efforts to develop a land-and-sea IPCA. IPCAs are a tool for the Nation to address ongoing limitations of state protected area governance and management, to better reflect the Nation’s Indigenous rights and responsibilities, and to preserve cultural heritage and biological diversity while fostering sustainable economic opportunities. The Kitasoo/Xai’xais process benefits from research on other IPCAs, includes intergenerational community engagement, and is rooted in long-term territory planning and stewardship capacity building. The Kitasoo/Xai’xais IPCA faces challenges similar to other protected areas but is influenced by ongoing impacts of settler-colonialism. The Kitasoo/Xai’xais Nation applies Indigenous and western approaches along with responsibility-based partnerships to address many anticipated challenges. Our case study demonstrates that more efforts are needed by state and other actors to reduce burdening Indigenous Nations’ protected area governance and management and to create meaningful external support for Indigenous-led conservation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".