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Record W1801821070

Multidimensional Poverty in Indonesia: Trends, Interventions and Lesson Learned

2008· book· en· W1801821070 on OpenAlexaboutno aff
Sudarno Sumarto, Wenefrida Widyanti

Bibliographic record

VenueMunich Personal RePEc Archive (Munich University) · 2008
Typebook
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsPovertySanitationVulnerability (computing)Basic needsMillennium Development GoalsEconomic growthPopulationQuarter (Canadian coin)Poverty thresholdConsumption (sociology)Chronic povertyIndonesianDevelopment economicsMalnutritionGovernment (linguistics)Psychological interventionSocioeconomicsGeographyEconomicsEnvironmental healthMedicinePoverty reductionSociology
DOInot available

Abstract

fetched live from OpenAlex

Despite the Government of Indonesia’s commitment to address human security as stated in its ambitious medium-term development plan and the Millennium Development Goals, poverty in its multidimensionality remain a major issue in Indonesia as a significant proportion of the Indonesian population is still consumption poor. Whilst the number of the poor has been decreasing consistently since 2002, most of those escaping poverty are still vulnerable and just a small shock can send them quickly below the poverty line. Using the PPP $2/day poverty line as a vulnerability measure, the World Bank (2006) found that 45% of Indonesians remain vulnerable to poverty. Nonconsumption poverty is even more problematic which includes malnutrition, maternal health, and access to basic services. For example, a quarter of children below the age of five are malnourished, only about 72% of births are accompanied by skilled birth attendants, 45% of poor households have no access to sanitation, more than half have no access to safe water, and around 20% of children from these households do not continue to junior secondary school.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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
GenreOther

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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueMunich Personal RePEc Archive (Munich University)Same topicPoverty, Education, and Child WelfareFrench-language works237,207