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Record W2487217058 · doi:10.5539/jsd.v9n4p113

International Migration, Livelihood Strategy, and Poverty Cycle

2016· article· en· W2487217058 on OpenAlexvenueno aff
Martua Sihaloho, Ekawati Sri Wahyuni, Rilus A. Kinseng, Sediono M. P. Tjondronegoro

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAgrarian societyLivelihoodSocial mobilitySocial classPovertyMiddle classUpper classSociologyEconomicsDevelopment economicsPolitical scienceEconomic systemGeographyEconomic growthSocial scienceMarket economyAgriculture

Abstract

fetched live from OpenAlex

Poverty drove Indonesian poor households (e.g. their family members) to find other livelihoods. One popular choice is becoming an international migrant. This paper describes and analyzes the change in agrarian structure which causes dynamics in agrarian poverty. The study uses qualitative approach and constructivism paradigm. Research results showed that even if migration was dominated by farmer households from lower social class; it also served as livelihood strategy for middle and upper social classes. Improved economics brought dynamics on social reality. The dynamic accesses to agrarian resources consist of (1) horizontal social mobility (means that they stay in their previous social class); (2) vertical social mobility in the form of social climbing; low to middle class, low to upper class, and middle class to upper class; and, (3) vertical social mobility in the form of social sinking: upper class to middle class, upper class to lower class, and middle class to lower class. The dynamic in social classes indicates the presence of agrarian poverty cycle, they are social climbing and sinking.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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