Criteria and Indicators for Evaluating Social Equity and Ecological Integrity in National Parks and Protected Areas
Why this work is in the frame
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Bibliographic record
Abstract
There are concerns that many national parks and protected areas worldwide are ineffective at protecting biological diversity and ecosystem processes, are socially unjust in their relations with Indigenous communities, or both. This paper outlines what we believe are the key criteria and indicators for evaluating social equity and ecological integrity in terrestrial national parks and protected areas. These criteria and indicators were developed through: (1) a detailed review of relevant literature; (2) a pilot analysis of the management plans and management direction statements from 14 national and provincial parks in Canada, Australia, and South Africa (countries with robust and extensive national parks systems and which share a common legacy of land dispossession followed by the subsequent pursuit of land claims by disadvantaged groups); and (3) an in-depth case study examination of six national parks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it