Looking for a Life: Nigerian Students Discuss Their Decisions to Study in China
Bibliographic record
Abstract
The decision to migrate for (ostensibly) educational purposes, is often accompanied by psycho-social feelings of fear, sadness, guilt, pride, happiness and courage. In this report, which is part of a larger study concerning Nigerian student migration to China, five Nigerian university students discuss their motivations for leaving home and studying in China. Students were interviewed on several occasions either on the campus of their university in Guangdong province, China, or in another convenient location near the campus. Narratives were transcribed and examined for commonalities in terms of reasons given for leaving Nigeria, and affective psycho-social feelings surrounding students’ decisions. Narratives are presented in first person accounts and coded for categorical content and episodic form. Episodic form is then graphed, not for quantitative analysis, but to show the positive, neutral and negative affective emotion, displayed during discussions on specific topics. Results reveal a high degree of pride in personal ability, and in the industriousness of kin. They also reveal happiness and a sense of satisfaction by participants in moving their lives forward, and being able to help family members in Nigeria. However, there were also feelings of sadness, anger and frustration at Nigeria’s poor economy, which participants believe is the result of government ineptitude and corruption. This study is limited in that it only considers male Nigerian migrants of the Igbo tribe, studying in Guangdong province. Future researchers are advised to widen the geographical area, include other Nigerian tribal members, and women.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".