Seeking Social Equity in National Parks: Experiments with Evaluation in Canada and South Africa
Bibliographic record
Abstract
"Many national parks (NPs) and protected areas (PAs) worldwide are operating under difficult social and political conditions, including poor and often unjust relations with local communities. Multiple initiatives have emerged as a result, including co-management regimes and an increased emphasis on the involve-ment of indigenous people in management and conservation strategies more broadly. Yet, controversy over what constitutes an appropriate role for local people persists, and little research has been conducted as yet to systematically evaluate the extent to which NPs are socially (and not just ecologically) effective. This paper discusses a first attempt to examine the efficacy with which NPs address social equity, includ-ing property and human rights, and the relationship of indigenous people and NP managers. The results from an evaluation of equity in a purposive sample of six NPs in Canada and South Africa are presented. All but one of the case study NPs is found to be achieving or moving towards equity. In particular, NPs with more comprehensive co-management and support from neighbouring indigenous groups demonstrate higher equity scores across a variety of indicators, whereas NPs with lower levels of co-management do less well. NPs with settled land claims have not necessarily been more equitable overall, and a few NPs have been co-managed in name only."
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".