Neoliberal multiculturalism and ethnic entrepreneurial self: A transnational perspective on ethnicity in China
Bibliographic record
Abstract
This research challenges the methodological nationalism that dominates studies of ethnic minorities in China, which often focus on how power dynamics within the nation-state shape ethnic identity formation. Drawing on discourse analysis, ethnographic fieldwork, and interviews with Hui Muslims in Yiwu—China’s global trade hub—this article adopts a transnational approach to examine how the state and minority individuals construct ethnicity. Building on theories of neoliberal multiculturalism and ethnic capital, I argue that China’s integration into the global economy has produced a discourse of neoliberal multiculturalism that assigns global market value to minority groups’ ethnic capital. Hui Muslims engaged with this state discourse to strategically construct an ethnic entrepreneurial self. I show how neoliberal multiculturalism served as a cultural repertoire to facilitate or constrain Hui Muslims’ efforts to negotiate symbolic hierarchies and state power. These findings shed light on how economic globalization reshapes ethnic minority people’s social positioning. The article also contributes to the theory of neoliberal multiculturalism by extending its analysis beyond state governance, exploring how it has functioned as a repertoire for transnational ethnic actors to negotiate self-identity and status inequalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".