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Record W4381191411 · doi:10.3126/scholars.v5i1.55806

Role of Foreign Employment in Socioeconomic Development of Beldandi Rural Municipality, Kanchanpur, Nepal

2022· article· en· W4381191411 on OpenAlexaff
Bir Bahadur Singh Thakuri

Bibliographic record

VenueScholars Journal · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsWestern University
Fundersnot available
KeywordsSocioeconomic statusRespondentSocioeconomic developmentSocioeconomicsRemittanceEconomic growthSurvey methodologyBusinessGeographyEconomicsPolitical scienceSociologyDemographyPopulationMedicine

Abstract

fetched live from OpenAlex

This study is about the role of foreign employment in socio-economic development of Beldandi rural municipality. The main objective of this study is to analyze the nature and the role of foreign employment in the socio-economic development of Beldandi Rural Municipality of Kanchanpur district. A micro-level case study type research design was applied in which primary data were collected from a household survey with random sampling methods. A semi-structured questionnaire was applied for primary data where secondary data related to foreign employment were collected from different published sources. Simple mathematical procedures were used to analyze the data. The study findings show that 85 percentage of households got economic security by increasing their economic status from remittance income, and 65 percentages of respondents improved their knowledge and skills and used their skills in their community after arrival. This study also shows that foreign employment has positive changes in the socioeconomic status of respondents. However about 11percent of respondents’ economic status is worse, 35 percent of respondents’ socioeconomic status did not changes and around 54 percent of the respondent’s social attitude changed due to foreign employment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.233
Teacher spread0.209 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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