Conceptual and Practical Issues in Defining Protected Area Success: The Political, Social, and Ecological in an Organized World
Bibliographic record
Abstract
Placed within the people-park debates, the authors explore the complexities in defining protected area success. It is argued the selective focus on biodiversity as the only criterion for success often found in the broader literature has limited current discussions. The authors suggest the framing of protected area success should be seen as more multifaceted. Multiple perspectives and actors exist representing a number of interests at various scales across such domains as politics, economics, social legitimacy, scientific (ecological) knowledge. Each actor tends to highlight its own set of rationales. To illustrate their points, the authors present a case study from Quintana Roo, Mexico. They conclude by underscoring that it is the socio-political process of pursuing conservation itself that is likely more valuable to the efforts than a universally established notion of protected area success.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.093 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| 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".