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Record W4401828182 · doi:10.15578/jppi.30.2.2024.53-64

KERENTANAN SOSIAL-EKOLOGI MASYARAKAT PERIKANAN SKALA KECIL DI SELAT BUTON, SULAWESI TENGGARA

2024· article· id· W4401828182 on OpenAlexaff
Raymond Jakub, Luky Adrianto, Handoko Adi Susanto, Stuart Campbell

Bibliographic record

VenueJurnal Penelitian Perikanan Indonesia · 2024
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsImmunoPrecise (Canada)
Fundersnot available
KeywordsPhysicsParticle physics

Abstract

fetched live from OpenAlex

Masyarakat perikanan skala kecil merupakan komunitas yang sangat rentan terhadap dampak perubahan iklim. Pada umumnya, kerentanan ini disebabkan oleh tingginya tingkat paparan dan sensitivitas kelompok ini terhadap dampak langsung perubahan iklim, serta lemahnya kemampuan untuk beradaptasi. Penelitian ini bertujuan untuk (1) mengetahui tingkat kerentanan di dua kawasan di Selat Buton yaitu komunitas Pasi Kolaga dan Kapontori, melalui atribut sosial-ekologi, (2) serta mengindikasikan strategi adaptasi yang perlu dilakukan sebagai respons kolektif terhadap komponen-komponen di dalam analisis kerentanan. Sebanyak 19 variabel iklim, sosial dan ekologi digunakan di dalam penelitian ini. Setiap variabel dikelompokkan ke dalam lima komponen yang menjelaskan nilai kerentanan perubahan iklim yaitu komponen Paparan, Sensitivitas Sosial, Sensitivitas Ekologi, Kapasitas Adaptif Sosial dan Kapasitas Adaptif Ekologi. Kelima komponen ini dianalisis untuk menghasilkan nilai kerentanan untuk setiap komunitas. Nilai indeks kerentanan sosial-ekologi komunitas Pasi Kolaga adalah 0,520 dan komunitas Kapontori adalah 0,567, yang menunjukkan bahwa secara sosial-ekologi komunitas pesisir di Kapontori lebih rentan terhadap perubahan iklim. Variabel yang dihasilkan digunakan untuk menentukan indikasi rencana aksi adaptasi yang dapat dilakukan oleh kedua komunitas ini untuk mengurangi nilai kerentanan dan meningkatkan kapasitas adaptif. Pengelolaan perikanan skala kecil yang dikelola yang dengan mengedepankan peran masyarakat dalam kemitraan dengan pemerintah daerah, serta dan mencakup intervensi beragam dapat menjawab tantangan perubahan iklim secara terintegrasi. Small-scale fishing communities are highly vulnerable to the impacts of climate change. In general, their vulnerability is caused by the high level of exposure and sensitivity of this community to the impacts of climate change, as well as their low adaptive capacity. This study aims to (1) determine the vulnerability in two communities in the Buton Strait, namely the Pasi Kolaga and Kapontori, using socio-ecological attributes, (2) and indicate the adaptive strategies that are required as a collective response to each component in vulnerability analysis. A total of 19 climate, social and ecological variables were used in this study. Each variable is grouped into five components, namely Exposure, Social Sensitivity, Ecological Sensitivity, Social Adaptive Capacity, and Ecological Adaptive Capacity components. These five components were analyzed to generate a vulnerability index for each community. The socio-ecological vulnerability index of the Pasi Kolaga community (VS.E_PASI) is 0.520 and the Kapontori community (VS.E_KAPO) is 0.567, which indicates that socio-ecologically the coastal community in Kapontori is more vulnerable to climate change. The variables resulting from this study are used to indicate adaptive action plans that can be carried out by these two communities to reduce their vulnerability and increase their adaptive capacity. Small-scale fisheries management that is designed by prioritizing the role of the community in the management, in a partnership with local governments, and including interventions in various aspects can help in addressing the challenges of climate change in an integrated manner.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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