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Record W2947097831 · doi:10.5539/ach.v11n2p38

Leaving Home: Yemeni Students Discuss Study Abroad Migration

2019· article· en· W2947097831 on OpenAlexvenueno aff
Howard Lorne Martyn

Bibliographic record

VenueAsian Culture and History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPrideFeelingHonorNarrativeAngerSadnessSociologyPoliticsObligationPsychologyPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

The decision to migrate for educational purposes is often stressful, but for those leaving countries embroiled in major warring conflicts, the decision may be overshadowed by feelings of sadness, anger and loss. And for many, the ostensible purpose of migration - education, is overshadowed by the desire or need to leave for economic and security reasons. In such situations, migrants hope they can power through those negative feelings and emerge successful, and with familial honor intact. The narrative weapon used to defeat negative feelings are stories of pride and resourcefulness. In this study Yemeni students studying at a university in Guangdong Province, China were interviewed concerning their decisions to leave Yemen. Participants were between 20 and 30 years old: all were male. Most hailed from Aden or Sana’a or areas adjacent to those major cities and all aligned themselves with pre-1990 South Yemen, as described by their fathers. Narrative analysis revealed a striking similarity: stories of hopeful future redemption through economic opportunities found abroad. Indeed, participants revealed a consuming desire for economic success - an obligation that was energized by feelings of pride in being trusted with custodial duties of familial honor. The results are discussed qualitatively in terms of categorical content and episodic form. This study is limited in that it only includes Yemeni males aligned with pre-1990 South Yemen, and those who hail from Sana’a, Aden or nearby urban centers. Future studies should include women, and those who encompass wider political views and reside in rural areas.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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