Why local people do not support conservation: Community perceptions of marine protected area livelihood impacts, governance and management in Thailand
Bibliographic record
Abstract
Conservation success is often predicated on local support for conservation which is strongly influenced by perceptions of the impacts that are experienced by local communities and opinions of management and governance. Marine protected areas (MPAs) are effective conservation and fisheries management tools that can also have a broad array of positive and negative social, economic, cultural, and political impacts on local communities. Drawing on results from a mixed-methods study of communities on the Andaman Coast of Thailand, this paper explores perceptions of MPA impacts on community livelihood resources (assets) and outcomes as well as MPA governance and management. The area includes 17 National Marine Parks (NMPs) that are situated near rural communities that are highly dependent on coastal resources. Interview participants perceived NMPs to have limited to negative impacts on fisheries and agricultural livelihoods and negligible benefits for tourism livelihoods. Perceived impacts on livelihoods were felt to result from NMPs undermining access to or lacking support for development of cultural, social, political, financial, natural, human, physical, and political capital assets. Conflicting views emerged on whether NMPs resulted in negative or positive marine or terrestrial conservation outcomes. Perceptions of NMP governance and management processes were generally negative. These results point to some necessary policy improvements and actions to ameliorate: the relationship between the NMP and communities, NMP management and governance processes, and socio-economic and conservation outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".