MétaCan
Menu
Back to cohort
Record W2900101233 · doi:10.22452/mjes.vol55no2.1

Teen Marriage and Feeding Behaviour to Children in Indonesia

2018· article· en· W2900101233 on OpenAlexfundno aff
Indriyati Indriyati, Dwini Handayani

Bibliographic record

VenueMalaysian Journal of Economic Studies · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Security and Socioeconomic Dynamics
Canadian institutionsnot available
FundersAcademy of Nutrition and DieteticsMcGill UniversityUniversitas IndonesiaWorld Health Organization
KeywordsBreastfeedingIndonesianLogistic regressionPsychologyDevelopmental psychologyChild healthPediatricsBreast feedingEducational attainmentMedicineDemography

Abstract

fetched live from OpenAlex

One of the key determinants of child nutritional status during the critical window period (the first thousand days of life) is feeding practices, including exclusive breastfeeding, continued breastfeeding, and complementary feeding. The condition of mother, as the main child caretaker, will determine the child’s nutritional status and nutrition patterns. This research will focus on the effect of teen marriage age, which will be predicted through education, on infant and young child feeding behaviour. Previous studies mainly focussed on only one type of child feeding, but this study discusses three types of child feeding behaviour. This study uses the Indonesian Demographic and Health Survey data in 2012 and the method of analysis is binary logistic regression. The sample for this study is based on 4,177 married women from age 15 to 49 who currently has a first child aged 0 to 23 months. The result shows that mothers who married early and with lowest education attainment have a better behaviour of exclusive and continued breastfeeding, but the worst on complementary feeding.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMalaysian Journal of Economic StudiesSame topicFood Security and Socioeconomic DynamicsFrench-language works237,207