A Pluralistic Approach to Protected Area Governance: Indigenous Peoples and Makuira National Park, Colombia
Bibliographic record
Abstract
Based on a study of collaborative governance (Spanish cogobierno, literally co-government) in Makuira National Park overlapping with an Indigenous collective territory of the Wayúu people recognised by the Government of Colombia, we analyse how Indigenous rights and conservation interests are negotiated between the national parks authority and local Indigenous governing authorities. Recognised common interests provide a basis for collaboration in protected area governance even where conflicting interests exist. The arrangements arrived at by negotiation incorporate Indigenous commons governance and parks conservation objectives, enabling territorial use planning which allows for both conflict management and protected area management. Ongoing collaborative governance based on common interests, brings both benefits and tradeoffs. The Wayúu give up part of their self-determination rights and the Park gives up part of the ideals of ecosystem conservation based on biological criteria. The strategic alliance works because it is based on the recognition of the legitimacy of Indigenous governing authority, the Parks’ role in protecting Wayúu territory, and the complementary strengths of the two parties. The core message of the case is that designing an inclusive and broad practice of conservation requires an approach involving processes that acknowledge common interests and tradeoffs for both parties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".