A comparison of five stakeholders' perceptions of governance under Ontario Provincial Parks' management model
Bibliographic record
Abstract
Governance is widely discussed in various government sectors or agencies such as Health Care and Education and throughout the private sector. Yet, it is only recently that reference to governance with regards to parks and protected areas has come to the for-front within various political and ecological circles. Parks and protected areas are increasingly threatened by climate change and political influences and therefore, there is a current need to assess the design and operations of protected areas so that they can be properly managed for the changes that have and will continue to occur. The current study examined how five stakeholder groups perceived 12 governance factors under Ontario Parks’ management model. Results revealed that Ontario Parks’ management model is perceived as having good levels of governance for all 12 factors by the entire population and within each of the five stakeholder groups. Differences in perception were observed primarily between the Park Staff participants when compared to the Contractor and Local Resident participants
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".