The results of ecotourism policies in protected areas in Brazil and Canada
Bibliographic record
Abstract
Abstract Purpose of topic: Policies and definitions of ecotourism address, basically, three aspects: environmental conservation, environmental awareness of visitors, and involvement of local communities. From that approach, the objective of this study is to analyze the results of public policies for public use development in protected areas. Methodology and approach: Through case study methodology we analyzed two protected areas, Alto Ribeira Tourist State Park (PETAR), located in the State of Sao Paulo, in Brazil, and Strathcona Provincial Park, situated in the province of British Columbia, Canada. The multiple case study was based on two sources of evidence, the identified public policies documents, and interviews with the various stakeholders: government, local community, and visitors. The analysis of documents and interviews was performed through content analysis. The public policies were discussed within the conceptual and legal frameworks for protected areas and public use in both countries. Among the key findings, we observe that public policies focus on more permissive activities, as in the Canadian case, or on its restriction, like in Brazil, is not the most significant aspect for the conservation of the environment. Originality of the document: The outsourcing policy, already adopted by British Columbia and beginning to be implemented in São Paulo, has impacted more directly the three analyzed aspects.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".