Bibliographic record
Abstract
The paper examines the linguistic and cultural features Nigel Ng, a Malaysian comedian, utilized in his performance as Uncle Roger, a Cantonese English speaker, and the ensuing repercussions. The data analysis shows that a) Ng does not use all the features of Hongkong English, and his use of the features is inconsistent; b) Ng’s stylized English incorporates stereotypical linguistic features associated with several discrete ethnic varieties in the pan-Asian area; c) Ng's performance of Uncle Roger is a conscious media strategy to connect with a more diasporic audience. This paper therefore argues that Ng's performance is a racial stylization of pan-Asian. On the one hand, it challenges White hegemony by constructing a proud Asian identity and criticizing the misrepresentation of Asian cultures. On the other hand, it also reproduces stigmatized stereotypes by foregrounding out-group stereotypes of pan-Asians. The racial stylization discussed in this paper illustrates the double-edged nature of stylization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".