Park visitors' perceptions of governance: a comparison between Ontario and British Columbia provincial parks management models
Bibliographic record
Abstract
Purpose The purpose of the paper is to compare visitor perspectives of the governance of two of Canada's largest park systems: the parastatal model of Ontario Provincial Parks and the public and for‐profit combination model of British Columbia Provincial Parks. Design/methodology/approach The authors developed an electronic survey based on the ten UNDP criteria of governance: strategic vision, accountability, transparency, consensus‐orientation, public participation, efficiency, effectiveness, responsiveness, equity, and rule of law. The survey was administered to park visitors for both park systems in the summer of 2008 and spring of 2009 (British Columbia Provincial Parks n=112, Ontario Provincial Parks n=255). Findings Researchers determined that the ten governance sections of the survey actually form 11 governance factors. Data suggested statistically significant differences in regards to the visitors' perceptions between the two park systems. Specifically, visitors to Ontario Parks ranked all 11 criteria of governance higher, closer towards good governance, than did visitors to British Columbia Parks (p<0.001). Practical implications These results suggest that the Ontario Parks parastatal model is closer to the ideals of good governance as perceived by the park users, when compared to the British Columbia parks' public and for‐profit combination model. This paper also provides future policy makers with a new understanding of the multiple factors that affect visitors experience and perceptions of protected areas. Originality/value This is one of the first studies to investigate visitors' perceptions of two commonly used protected area management models. These research findings contribute to the debate regarding which protected area management model is superior when compared using the UNDP governance criteria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".