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Record W3082903662 · doi:10.5539/ass.v16n9p1

The Roles of Women in Food Security in South Merapi Slope Villages

2020· article· en· W3082903662 on OpenAlexvenueno aff
Hastuti Hastuti, Edi Widodo

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityLivelihoodDiversification (marketing strategy)PovertyBusinessQuality (philosophy)Economic growthSocioeconomicsMarketingEconomicsGeographyAgriculture

Abstract

fetched live from OpenAlex

Economic conditions and poverty in rural areas have become problems in meeting the needs of food as the most basic needs/need. This problem can lead to food insecurity. This research aims to: (1) examine the characteristics of women; (2) study the obstacles faced by women in achieving food security; and (3) investigate women's efforts to achieve food security. The data were analyzed using quantitative descriptive technique by means of frequency tables. The livelihood diversification in Jetis Suruh was more visible than that in Bulus Lor. The fulfillment of individual food needs was related to economic, social, and cultural conditions. The year-round food needs of both villages indicated the need for food throughout the year. The need for food throughout the year in Bulus Lor was relatively better than that in Jetis Suruh. In general, food security in Bulus Lor was better than that in Jetis Suruh. Food security included the quantity and quality of food that met the standard of living of all family members. The availability of food in every household experienced dynamics at a certain time. When confronted with the limited food availability challenge, food for fathers was prioritized and this was dominant in both villages. Strategies to expand the diversification of businesses undertaken to increase household incomes included mobilizing all household members to go to work, borrowing money to make ends meet, saving money, reducing food, reducing the quality of food consumption, migrating jobs, and asking for help from family through friendship.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.088
GPT teacher head0.408
Teacher spread0.320 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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