Indicators for assessing good governance of protected areas: Insights from park managers in Western Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 it