Spousal Conflicts among Nigerian International Migrants: Contexts of Conflict Management and Support System
Bibliographic record
Abstract
This study investigated the causes of spousal conflicts, conflict management approaches, and roles of kinship and social networks among Nigerian International Migrants. While the study adopted Albert Bandura’s Social Learning Theory as a theoretical framework, the exploratory cross-sectional research design was employed. Australia, Canada, the European Union, the United Kingdom, and the United States of America were purposively selected owing to the significant presence of Nigerian citizens in these countries. Purposive and snowball sampling techniques were used to select participants. Influenced by the principle of saturation, in-depth interviews were conducted with 37 participants. The study established that parenting and child-rearing practices, financial strains and obligations, cultural adaptation challenges, external stressors, traditional gender roles and expectations, and infidelity were some of the causes of spousal conflicts among Nigerian International Migrants. Also, spousal conflict was managed through communication-based approaches, cultural and family interventions, and professional and community support systems. Though kinship plays a good role in solving spousal conflicts, it is often considered negative when kinship interventions get extreme and breach privacy. Through intervention from influential family members, kinship manages spousal conflicts. At the same time, social networks provide emotional support to couples during challenges but can also exacerbate spousal conflicts through biased advice and peer pressure. It is recommended that state and non-state actors should pay closer attention to policies and interventions to better govern migration and mitigate the social costs through the nodes of spousal conflicts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".