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Record W4406203792 · doi:10.1007/s43621-025-00810-z

From vulnerable to resilience: an assessment of small-scale fisheries livelihood in South Malang of Indonesia

2025· article· en· W4406203792 on OpenAlexaff
Puji Handayati, Ahmad Munjin Nasih, Indah Susilowati, Idris Idris, Prateep Kumar Nayak, Bagus Shandy Narmaditya

Bibliographic record

VenueDiscover Sustainability · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLivelihoodResilience (materials science)FisheryScale (ratio)GeographyEnvironmental resource managementEnvironmental scienceAgricultureBiologyCartography

Abstract

fetched live from OpenAlex

The livelihoods of small-scale fisheries in South Malang of Indonesia are being notably influenced by ecosystem degradation and climate change. Therefore, this study aims to investigate the vulnerability of small-scale fisheries in South Malang of Indonesia using the livelihood vulnerability index. The livelihood vulnerability was estimated using five major components and 20 sub-components. The data were obtained from small-scale fisheries in South Malang of Indonesia through questionnaires directed to the respondents using simple random sampling. The findings indicate that two main components (livelihood strategy and climate variability and natural disaster) were included in high vulnerability, while another two main components (sociodemographic and social network) were categorized as moderate vulnerability. The sensitivity components, including access to water, electricity, and health) were perceived as low vulnerability. Based on the findings of this study, it recommends that policymakers need to develop policies and campaigns regarding climate awareness and adaptation strategies by small-scale fisheries.

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)0.000
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 score0.034
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.273
Teacher spread0.266 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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