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Record W2126426960 · doi:10.5539/enrr.v3n1p24

Increasing Public Participation through Awareness Raising Activities: A Case Study in Trao Reef Marine Reserve, Vietnam

2012· article· en· W2126426960 on OpenAlexvenueno aff
Hang Thi Minh Tran, Loke Ming Chou, Hue Thu Nguyen

Bibliographic record

VenueEnvironment and Natural Resources Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodStakeholderBusinessStakeholder engagementEnvironmental resource managementEnvironmental planningNatural resource managementCommunity participationLocal communityNatural resourceProcess (computing)Political scienceGeographyPublic relationsSocioeconomicsAgriculture

Abstract

fetched live from OpenAlex

In the last few decades, community-based management has evolved to become an effective approach in managing natural resources and solving environmental problems around the world. In this approach, stakeholder participation is considered as a very important factor contributing to the success or failure of the management effort. To promote and enhance stakeholder participation, awareness raising activities could be conducted in various innovative forms. This is clearly illustrated in the case of Trao Reef, a locally-managed marine reserve in central Vietnam. The paper reviews the management approach applied to Trao Reef, focusing on analysis of stakeholder participation. The high level of environmental awareness of the local community contributed to the successful outcome of the project. Many communication activities and awareness raising campaigns were organized within the community to get them involved in all steps of the management process. The project enhanced the marine resources and ecosystem health, and helped to build a more sustainable livelihood and living lifestyle for the local community. Trao Reef has become a role model for effective environmental management that harmonizes the socio-economic benefits of the local community and nature conservation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.100
GPT teacher head0.360
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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