Accountability, Conservation and Community: Measuring the Local Economic Impacts of Protected Areas
Bibliographic record
Abstract
As a signatory to the Convention on Biological Diversity, Canada has committed to help protect biodiversity through an increase in effectively and equitably managed systems of protected areas (PAs) by 2020. If Canada fulfills this commitment, the country will see the largest expansion of PA networks in its history. Although employing ‘equitable and effective management’ suggests PAs have a responsibility to consider their effects on local stakeholders, on the whole, Canada’s PA agencies do not publicly and systematically report on their jurisdictions’ local economic impacts. To address this gap, this thesis aims investigate mechanisms for PA agencies to identify and consider ecological-economic intersections within their regions, in order to inform approaches for PA managers to conduct community consultations related to their impacts. Twenty-seven participants from municipalities in the Riding Mountain Biosphere Reserve used mapping activities, surveys and interviews to identify features they perceived to economically impact their area and wellbeing. Participants reported that tourism and agriculture were important to the economic fabric of the region; that activities involving wildlife (i.e. fishing and wildlife viewing) and the region’s cultural diversity helped generate local tourism; and that Riding Mountain National Park’s management decisions had varying effects, but tended to hold greater benefits for jurisdictions closer to the park’s central administration. The results underscored the importance of approaching stakeholder relationships geographically, since the impacts of decisions made by PA managers are felt differently among locals depending on their location around the PA boundary. To maintain constructive relationships between PAs and their local stakeholders, it is recommended that PA administrations undertake systematic community ii consultations accompanied by subsequent self-reporting. It is further recommended that efforts be made to incorporate maps into community consultation processes.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".