Exploring opportunities and constraints associated with protected areas in Northern Ontario, Canada / by Izabela Anna Wozniczka.
Bibliographic record
Abstract
In recent years, there have been increasingly more protected areas established as an attempt to preserve the biodiversity of coastal and terrestrial ecosystems. In Canada, government agencies have set a goal to protect 12% of natural areas by creating protected areas such as national parks, marine protected areas, national wildlife areas, marine wildlife areas, and migratory birds sanctuaries. Recently, in Ontario, the first national marine conservation area (NMCA) was established on the north shore of Lake Superior. The National Marine Conservation Areas (NMCA) Program is designed to protect and conserve a network of areas representative of Canada's marine environments for the benefit of present and future generations (Parks Canada, 2006). Aside from the obvious benefits of protecting areas of significant biological diversity, research has documented the negative and positive impacts of protected area development on local communities: conflicts over land rights, different understandings of the relationship between people and protected areas, the preservation of natural communities, and the protection of historical and cultural resources. \nThe purpose of this study was to investigate the opportunities and constraints associated with tourism in protected areas as perceived by the affected community stakeholders in communities adjacent to the newly established Lake Superior National Marine Conservation Area (LSNMCA). Five themes have emerged: sense of place, trust/relationship with senior \nlevels of government, recreational and traditional importance of the region, views of tourism and development and fear. The participants identified several opportunities and constraints. The opportunities included utilizing the natural resources and landscape of the area, increasing investment in recreational activities in the area, and developing tourism. The constraints included a complex relationship with the government, legislation and policies, a traditional bureaucratic approach to management, and a lack of funding. Since the LSNMCA initiative was first introduced, local attitudes have positively changed towards potential tourism development in the area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".