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Record W2561691440 · doi:10.1142/s1084946716500278

EXAMINING ADAPTATION STRATEGIES OF SUB-SAHARAN AFRICAN IMMIGRANT ENTREPRENEURS IN CHINA: THE CASE OF GUANGDONG

2016· article· en· W2561691440 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Developmental Entrepreneurship · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversité LavalToronto Metropolitan University
Fundersnot available
KeywordsChinaImmigrationFace (sociological concept)Adaptation (eye)Qualitative researchPolitical scienceAdventureEconomic growthSouthern chinaDevelopment economicsGeographySociologyPsychologySocial scienceEconomicsHistory

Abstract

fetched live from OpenAlex

This paper examines how sub-Saharan Africans do business in China, particularly in the province of Guangdong. Through a qualitative approach, the paper analyzes data obtained from twenty interviews with sub-Saharan Africans. It’s a descriptive study that explores the strategies, tactics and attitudes adopted by those sub-Saharan Africans to cope with a particularly difficult Chinese business environment. Using the concepts of foreignness and adaptation, the study identified four categories of immigrant entrepreneurs: the assimilators, the conservatives, the adventurers and the cautious. Concomitantly, this research identified factors and skills that contributed significantly to immigrants’ success in China. The paper also underlines challenges sub-Saharan Africans still face in China and the unconventional tactics they use. The study represents an insightful exploration of an increasingly important subject but still under-studied. It calls for a thorough research toward the understanding of African businesses in China.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.269
Teacher spread0.227 · 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