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Record W3013276993 · doi:10.26522/ssj.v2020i14.1872

Unequal Interdependency: Chinese Petty Entrepreneurs and Zimbabwean Migrant Labourers

2020· article· en· W3013276993 on OpenAlexafffundvenue
Ying-Ying Tiffany Liu

Bibliographic record

VenueStudies in Social Justice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Johannesburg
KeywordsRacializationMigrant workersPoliticsMainstreamSociologyInterdependenceNationalityGender studiesPolitical scienceEconomic growthDevelopment economicsImmigrationEconomicsSocial scienceRace (biology)

Abstract

fetched live from OpenAlex

Exploring the cultural politics of diasporic entrepreneurs and migrant labourers through an examination of Chinese restaurants in Johannesburg, this article presents what I call the “intra-migrant economy” amid everyday racialized insecurities in urban South Africa. I use the term “intra-migrant economy” to refer to the employment of one group of migrants (Zimbabwean migrant workers) by another group of migrants (Chinese petty capitalists) as an economic strategy outside the mainstream labour market. These two groups of migrants work in the same industry, live in the same city, and have established a sort of unequal employment relation that can be hierarchical and interdependentat once. Chinese migrants are socially marginalized but not economically underprivileged, which stands in contrast to Zimbabwean migrants, who remain economically underprivileged even though they speak local languages. Their different socioeconomic positions in South Africa are profoundly influenced by their nationality and racialization. Thisanalysis of their interdependency focuses on the economic and political structures that shaped the underlying conditions that brought Chinese and Zimbabwean migrants to work together in South Africa.

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)0.002
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 score0.037
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.053
GPT teacher head0.368
Teacher spread0.315 · 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

Citations4
Published2020
Admission routes3
Has abstractyes

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