A qualitative inquiry into the socio-economic conditions of Nigerian immigrant families in Canada
Bibliographic record
Abstract
Immigration has been used by governments to provide labour to resolve shortages in many occupations across many countries. The arrival of people, especially to high income countries like Canada and United States, has been largely regulated to accept applicants with the desired skills to meet the labor demand. Almost two-thirds of all immigrants to Canada are part of this “economic stream” of “selected” migrants. What these economic explanations fail to consider is that for immigrants, the imperative to migrate is not solely based on individual economic motives. Considering the needs of families is not only important for immigrants themselves but has been shown to improve general integration and reduce the chance of subsequent secondary migration. This thesis examines the socio-economic conditions of recent Nigerian immigrant families to Winnipeg, Canada. The study specifically explores immigrants’ motivation for migration, lived experiences, expectations and challenges by examining the family as a unit of analysis. Most existing studies have been limited to focusing on quantitative methods in studying the experiences of individual immigrants without paying any attention to the important family contexts and outcomes of migration. This study uses a qualitative research design using interviews with eleven participants. The New Economic of Labour Migration theory is employed to study the households. Three themes emerged from the interviews: economic conditions are not the only reason for moving to Canada, life satisfaction, and future outlook. The study revealed that Nigerian immigrants’ motivation for migration is primarily based on economic considerations for their families, but that other motives include better life for children, security and prospects for their future. Almost all immigrants settled for jobs which did not match their qualifications and experiences upon arrival. However, their situation improved as time in Canada increased. Most of the participants were optimistic about their future in Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".