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Record W2770361200 · doi:10.5539/ass.v13n12p35

Impact of Sago Crop Commercialization Programs on Gender Roles of Melanau Communities in Sarawak, Malaysia

2017· article· en· W2770361200 on OpenAlexvenueno aff
Siti Zanariah Ahmad Ishak, Malia Taibi, Ahmad Nizar Yaakub

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
FundersUniversiti Malaysia SarawakUniversity of Oxford
KeywordsCommercializationLivelihoodBusinessAgricultureProduction (economics)Ethnic groupHuman capitalEconomic growthGender analysisOrder (exchange)GeographyPolitical scienceSocioeconomicsMarketingEconomics

Abstract

fetched live from OpenAlex

Melanau men are known for their significant roles in the cultivation of sago palm as smallholder farmers while the women take charge of processing sago-based food products. Melanau sago farmers play important roles in maintaining their rural livelihood as the ethnic minority group in the northwest coastal communities of Sarawak, Malaysia. In an attempt to contribute to the corpus of knowledge on Melanau gender roles and their unique farming practices, this paper adapts gender relations framework in order to assess the impact of sago commercialization programs that were established by the local authority since 1980s. The findings revealed that the changes of traditional gender roles among men and women are influenced by gender relations factors i.e. gender division of labour, access to or control of resources and household decision making. In addition, sago production promises a greater prospect of moving away from low to high commercial level of production only if human capital that makes sago crop difficult to commercialize are tackled in the Melanau community. This suggests that more attention to human factors is needed when authorities formulate policies relating to commercialization program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.329
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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