Understanding and mapping local conflicts related to protected areas in small islands: a case study of the Azores archipelago
Bibliographic record
Abstract
Establishing Protected Areas (PAs) is considered one of the most appropriate ways to conserve nature and cultural landscapes. However, conservation constraints can generate social conflicts, especially at a local level. In small islands (SIs), local conflicts may escalate due to an increase in competition for limited space and resources. Pico island in the Azores Archipelago (Portugal), part of the Outermost European region, was considered a good case to study conservation-development conflicts due to the amount of designated protected land (> 35% of its surface) and the approval of a new Azorean PA network in 2007. This paper presents a new approach to understanding and mapping local conflicts within PAs in SIs by integrating qualitative data and spatially explicit information. This research takes stock of the benefits, needs and constraints related to Pico Natural Park as perceived by local stakeholders through face-to-face semi-structured interviews; it subsequently identifies and transposes the conflicts distilled from stakeholder discourse into spatially representative visual maps via GIS. Research outcomes show that PAs are perceived mainly as constraints to local development, showing inconsistency between local expectations and regional conservation policy. This highlights the importance of including public participation processes prior to any implementation of conservation strategies. The proposed method provides a springboard towards effective conflict management for PAs on Pico island, showing a relatively low-cost and straightforward approach to minimising future local conflicts which could be adapted to other similar Outermost European regions and SIs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".