The Whiteness of English: Raciolinguistic Chronotopes and Cultural Transformations in Contemporary China
Bibliographic record
Abstract
Abstract In the context of China’s ongoing modernization, language has become a key element of distinction marking certain citizens as wealthy, cosmopolitan, and authoritative. While local dialects have been marginalized at the expense of standard Mandarin, global languages such as English have been incorporated into the education system and this framework of language values. This chapter explores the racial dimensions of language usage, adopting Bakhtin’s concept of the chronotope—an articulation of language with space and time—to argue that within China’s discursive landscape, English has become inseparable from whiteness. By examining everyday conversations, texts, and narratives, I show how some forms of talk are associated with a sense of restrictive pastness, while others appear to emerge out of, and beckon from, an open and boundless future. Speakers position themselves within a racialized scheme that associates global languages with whiteness, social power and unfettered mobility. Chronotopes therefore offer a key method for connecting race, language and embodied life in contemporary China.
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".