Indicators for assessing good governance of protected areas: Insights from park managers in Western Australia
Bibliographic record
Abstract
Effective management of protected areas relies on good governance. An assessment was undertaken using the standards provided by the United Nations Development Programme's characteristics of good governance for sustainable development as a starting point. Being able to assess governance based on indicators is essential for ongoing effective management through improving practice. Although indicators and evaluation frameworks are available, they do not offer protected area managers a quick, comprehensive measure of governance. We used a three-round Delphi method with a cohort of 33 managers and researchers from government and non-government organizations, and universities. This participatory research process established a set of 20 indicators addressing public participation, consensus orientation, strategic vision, responsiveness, effectiveness, efficiency, accountability, transparency, equity, and rule of law. Accompanying output measures were provided by management plans, annual reports, audits, and stakeholder engagement. The findings emphasize the contributions of management plans and annual reports in establishing evaluation requirements and providing a place where results are publicly available. Further participatory research to refine these indicators and apply them in a diversity of contexts is advocated.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".