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Record W4392191723 · doi:10.1080/1369183x.2024.2323049

Middle-class youth fleeing Nigeria: rethinking African survival migration through the <i>Japa</i> phenomenon

2024· article· en· W4392191723 on OpenAlexafffund
JJ Liu

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMacEwan University
FundersMacEwan University
KeywordsRefugeeGender studiesElitePersecutionSociologyForced migrationCriminologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.360
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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