MétaCan
Menu
Back to cohort
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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
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 teacher head, not a consensus.

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

Explore more

Same venueAsian Social ScienceSame topicOil Palm Production and SustainabilityFrench-language works237,207