MétaCan
Menu
Back to cohort
Record W2613333377 · doi:10.5539/ilr.v6n1p109

Empowering Fishermen through Local Wisdom and Sustainable Development: a Policy Research

2017· article· en· W2613333377 on OpenAlexvenueno aff
Sukarmi Sukarmi

Bibliographic record

VenueInternational Law Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentWelfareGovernment (linguistics)BusinessFishingLocal governmentCompetition (biology)Environmental planningEconomic growthPolitical scienceEconomicsPublic administrationGeographyEcologyMarket economy

Abstract

fetched live from OpenAlex

The current study was to observe to what extent efforts are taken by the local government of Demak Regency, Central Java Indonesia to empower the fishermen based on local wisdom, as well as what model is right with the sustainable development. Technically, the government can take benefit from this study to issue a policy of ‘empowering and protecting fisherman with sustainable development model. The regency has bio and non-bio potential resources. However, due to the lack of visionary attention to the resources and the absence of the comprehensive maritime planning, the ecology and the socio-economy of the area are facing serious problems, such as unhealthy competition in fishing in its multiple manifestations contributing to the poor welfare of the fishermen along the coastline. In-depth interviews were held among 20 fishermen to find out their wishes for improvement of the welfare. It was concluded that policies of pro-fishermen have to be developed on the basis of local wisdom and sustainable development and recommendations were offered accordingly.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.063
GPT teacher head0.416
Teacher spread0.353 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Law ResearchSame topicMarine and Coastal EcosystemsFrench-language works237,207