International Migration, Kinship Networks and Social Capital in Southwestern Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".