Park Staff Members’ Perceptions of Governance: A Comparison between Private Sector and Parastatal Management
Bibliographic record
Abstract
Three main types of management models for parks and protected areas exist within Canada: the National Park Model, the Parastatal Model, and the Public and For-Profit Combination Model. Each of Canada’s two largest and most visited provincial park systems uses one of these three management models. British Columbia (BC) uses contractors for all visitor services (Public and For-Profit Combination Model) and Ontario (ON) largely uses park staff (Parastatal Model). There is debate on which of these approaches is preferable. This research compared these two management models by asking park staff members their opinions on how their agency satisfied 10 standard principles of governance (public participation, consensus orientation, accountability, transparency, responsiveness, effectiveness, efficiency, equity, rule of law, and strategic vision). The survey yielded 132 responses, 63 (47.7%) from ON Parks staff and 69 (52.3%) from BC Parks staff. Our analysis revealed 11 principles instead of the 10 originally considered. The results reveal that ON Parks staff have positive views on the governance of the ON Parks model for all 11 factors, while BC Parks staff have positive views of the BC model for 10 of the 11. ON Parks staff were significantly more positive than BC staff toward seven of the 11 criteria. BC Parks staff felt negatively toward the criterion of equity-finance. Given that the major justification for the use of private contractors in the delivery of park services in BC Parks is to augment financial equity, it is notable that park staff seemed especially concerned about the financial equity of this activity. This is among the first studies to examine the attitudes of park staff members toward the management models in which they work. We conclude that the differences found are due to two factors: the management models used and the relations between the park staff and their government. The research revealed that from the point of view of accepted principles of governance as perceived by park agency staff in Ontario and British Columbia, the Parastatal Model for park tourism management was viewed more positively than the Public and For-Profit Combination Model.
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.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".