Middle-class youth fleeing Nigeria: rethinking African survival migration through the <i>Japa</i> phenomenon
Bibliographic record
Abstract
Migration for survival is commonly associated with refugees and asylum seekers who flee persecution, wars, and natural calamities. Yet, in Nigeria, university-educated, gainfully employed middle-class youth insist that leaving is a matter of survival and not a choice. This distinction is signalled by japa, a recently popularised term for ‘to run’ or ‘to flee’. Young Nigerians view migration as an escape from intolerable domestic conditions – prolonged university strikes, overturned development progress, and unprecedented currency inflation. In practice, japa follows formal procedures but favours quick departures. But by framing migration as fleeing, youth emphasise their refusal to cope. Foremost, they emphasise urgent respite over rationalised projects of social reproduction or status maintenance. Scholars, however, tend to overlook emotional or existential motives for voluntary migration and essentialise survival drives to forced migration. Drawing on 21 interviews with Nigerian youths, this paper shows a need to rethink survival migration, particularly how we value destinations compared to departures, conflate urgent desires with immediate exits, and privilege social functions over individual sentiments. By analysing youth’s interpretations of what survival means, we enrich our understanding of African migration beyond the binary of elite strategies for social reproduction or fateful journeys of forced migration.
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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 | 0.004 | 0.003 |
| 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 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".