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Record W2946860132 · doi:10.1080/08865655.2019.1619475

International Migration, Kinship Networks and Social Capital in Southwestern Nigeria

2019· article· en· W2946860132 on OpenAlexvenueno aff
Ọláyínká Àkànle, Olufunke Fayehun, Gbenga S. Adejare, Otomi Augustina Orobome

Bibliographic record

VenueJournal of Borderlands Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsKinshipRemittanceSocial capitalInterpersonal tiesEconomic growthDeveloping countrySociologyDevelopment economicsPolitical scienceEconomicsSocial science

Abstract

fetched live from OpenAlex

International migration attracts global concern as international migration and its remittances are highly important mechanisms with profound implications for family, community, and national and international sustainability across borderlines. The demand for workers in most industrialized countries in order to sustain national economies and aspiration of migrants from less industrialized nations for better job opportunities and better ways of life have continued to foster migration and challenge constructions of social capital. As well as various push and pull factors, kinship networks and familial social relations serve as major drivers of migration. Consequently, various social structures and development projectiles in the giving and receiving nations are implicated. Thus, this study delved into interrogating the contours of how remittances in terms of patterns and perceptions embedded in migrations and social relations of migrants and their kin in selected locations in Ibadan. This study utilized a purely qualitative method of research because the subject matter focuses on making sense of meanings people attach to migration, remittance and supports as social capital towards understanding migration dynamics. Data were purposively collected through in-depth interviews in Ibadan metropolis, Nigeria. A total of 40 interviews were conducted. This article makes an important contribution to the data and literature on motivations to migrate.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.315
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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