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Record W3171920373

Socio-Economic Problems of Remittance Economy: The Case of Nepal

2017· article· en· W3171920373 on OpenAlexaff
Basu Sharma

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsRemittancePovertyDutch diseaseDevelopment economicsEconomicsCapital (architecture)Human capitalBusinessInformal sectorLabour economicsEconomic growthExchange rateGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

Nepal is one of the poorest but top remittance recipient countries in the world. Remittance has stood out as one of the key factors in reducing poverty, improving human capital and financing imports. However, remittance has also led to moral hazard and the Dutch disease phenomena through hollowing out of adult, productive household members and creating inefficiency in farm production due to shortage of labour and income substitution effect. Furthermore, it has contributed to increasing imports for luxury and semi-luxury goods, resulting in trade deficit. It has eroded competitive advantage of the country by weakening the export sector of the economy. At the micro-level, remittances have been responsible for family breakdowns, erosion of family values, exploitation and inhuman treatment of migrant workers by employers, and problems of reintegration of migrants upon return. This paper examines these issues so as to inform policymakers and researchers.

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 score0.033
Threshold uncertainty score0.065

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 studies0.0070.004
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.002
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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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