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Record W2890856643 · doi:10.1080/14649365.2018.1514647

African migrants in China: space, race and embodied encounters in Guangzhou, China

2018· article· en· W2890856643 on OpenAlexaff
Kelly Si Miao Liang, Philippe Le Billon

Bibliographic record

VenueSocial & Cultural Geography · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Global Influence and Migration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRacializationEmbodied cognitionChinaGender studiesSociologyRace (biology)Geography

Abstract

fetched live from OpenAlex

This paper examines ‘intimate geographies’ of everyday social encounters between African migrants and Chinese residents in Guangzhou, China. Based on interviews in an urban area represented as an ‘African enclave’, we document some of the banal, everyday sensory and corporeal encounters relating to housing, mobility, food, gender and trade. We suggest that African migration does not easily constitute an economic and cultural ‘bridge’ facilitating comprehension and appreciation between ordinary Chinese and Africans. Rather, we find racialized ‘Othering’ of African migrants to be a prevalent feature of encounters. We also find that African migrants are not voiceless and passive but proactive in questioning these views and practices, and in seeking to expand and deepen economic and broader social ties. These findings point to the importance of sensory perceptions and corporeal practices shaping racialization in many spheres of life, but do not preclude some forms of cultural bridging and positive interactions, demonstrating the ambivalences of embodied encounters in a globalizing city.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.004
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.302
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations49
Published2018
Admission routes1
Has abstractyes

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