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Record W4319079632 · doi:10.55365/1923.x2022.20.103

Sustainable Development Goals for Empowering Women Fishers Through Mangrove Use

2023· article· en· W4319079632 on OpenAlexvenueno aff
Ani Purwanti, Dyah Wijaningsih, Muh. Afif Mahfud, Aga Natalis

Bibliographic record

VenueReview of Economics and Finance · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsnot available
FundersUniversitas Diponegoro
KeywordsEmpowermentGovernment (linguistics)StatuteBusinessLegal researchSustainable developmentInformal sectorFishingEconomic growthSocioeconomicsPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

The Protection and Empowerment of Fishermen, Fish Cultivators, and Salt Farmers Act of 2016 protect small fishermen by requiring the government to give financial assurances if harvest yields are low.This law does not recognise or demand affirmative action for women fishermen to obtain equal access to protection and empowerment programmes.This forces women fishermen, culturally segregated from the fishing sector, into the home.Indonesia's Sustainable Development Goals (SDGs) include gender equality.This study examines gender imbalance in Law 7 of 2016's fisherman support programme in Brebes' Mangrove area.In this place, women fishermen can empower themselves through mangroves and fish farming despite Law 7 of 2016's policy vacuum.The socio-legal study examines the role of laws, rules, legal policies, and other legal systems in people's lives, including non-legal variables.In Brebes Regency, the primary concern is the lack of a statute that accommodates women fishers.This study uses socio-legal and descriptive analysis.From this research, it is hoped to learn about the implications of and not yet maximal programmes for empowering women fishermen according to Law Number 7 of 2016, which affects their economic and social life, and how these women fishermen have opportunities and equality (Gender Equality) so they can empower themselves among the people of Brebes Regency in particular and Central Java in general.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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 designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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