MétaCan
Menu
Back to cohort
Record W1873183252

The Impact of A National Poverty Reduction Program on Ethnic Minorities in Vietnam: The Lens of Baseline and Endline Surveys

2012· preprint· en· W1873183252 on OpenAlexfundno aff
Cuong Nguyen, Thu Phung, Tung Phung, Ngoc Minh Vu, Daniel Westbrook

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersEgg Farmers of Canada
KeywordsEthnic groupPovertyProductivityPer capitaSocioeconomicsHousehold incomePer capita incomeAsset (computer security)Government (linguistics)AgricultureEconomic growthGeographyEconomicsDevelopment economicsPolitical scienceDemographySociologyPopulation
DOInot available

Abstract

fetched live from OpenAlex

To increase the opportunities for poor ethnic minorities to benefit from economic growth the government of Vietnam implemented one of the biggest poverty reduction programs entitled ‘Socio-economic Development for the Communes Facing Greatest Hardships in the Ethnic Minority and Mountainous Areas’ during 2006-2010. This paper provides empirical evidence of this program’s impacts on households in the project areas. We find that theprogram had positive impacts on several important outcomes of the ethnic minority households, including productive asset ownership, household durables ownership, and rice productivity. Among higher-order outcomes, they enjoyed positive impacts in income from agriculture, household total income, and household per-capita income. A particularly important result is that poverty among minority households in treatment communes declined significantly more than it declined in comparison communes. Finally, ethnic minority households enjoyed a reduction in travel time to health facilities, relative to households in control communes.

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)0.007
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.068
GPT teacher head0.329
Teacher spread0.262 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich)Same topicIncome, Poverty, and InequalityFrench-language works237,207