Evaluating Management Performance and Effectiveness for Protected Areas
Bibliographic record
Abstract
Introduction This paper proposes an alternative to evaluating management effectiveness, by accounting separately the management and governance aspects, according to whether achieving the outcome is more in control of the park or the agency. The alternative, called the Ecosystembased Management System, combines principles from ecosystem-based management and environmental management systems. It was developed from case studies from Mexico and Canada. The adaptive management review within the EBMS provides effectiveness scores for individual management objectives, components, modules, and the overall park management. The scores indicate the degree of achievement of expected outcomes for specific objectives, measured through indicators and targets. The EMBS model helps integrate the uniqueness of individual parks and track management effectiveness on the long term for individual parks and the whole system of parks. The need for more accountability in natural resources and protected areas management has led organizations, such as the International Union for Conservation of Nature (IUCN), to release frameworks to evaluate management effectiveness (Hockings, Stolton ,and Dudley 2000). However, countries such as Canada and Mexico do not use the framework and park agencies in countries such as Spain and Argentina are adopting ISO standards (ISO 1996, 2000) to improve management or reduce environmental impacts from operations (M.Batiste and M. Di Paola, pers. comm.; PCA 2002). Park agencies are struggling to evaluate effectiveness because of the diversity of indicators involved. Here, we present the model of an ecosystem-based management system for protected areas (EBMS) that combines principles from ecosystem-based management (EBM) and environmental management systems (EMS), approaches adopted to improve management in natural resource and business organizations, respectively (Mendoza, Quinn, and Thompson 2004). The purpose of the EBMS is to assist parks managers on the planning process and to facilitate the evaluation of management effectiveness through the integration of different types of indicators.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".