Metrolingual multitasking and differential inclusion: Singapore’s Chinese languages in shared spaces
Bibliographic record
Abstract
Arrival cities are defined through migration-led diversification that structures integration, notably through everyday language practices. In Singapore’s multilingual landscape, we find hints of historical waves of migrants from Southern China speaking Cantonese, Hakka, Hokkien and Teochew and the recent contributions of new migrants from Mainland China. In light of the work of Pennycook and Otsuji, this article explores how the norms of metrolingual multitasking – of adaptation through language – structure differential inclusion in Singapore through banal and commonplace interactions in shared spaces, such as markets. By focusing on historically situated linguistic scripts of inclusion and exclusion in the city-state, we contrast the linguistic adaptations of older and newer arrivals to show how integration is continuously constituted through the differential inclusion of new arrivals. Based on a series of interviews, we shed light on how metrolingual multitasking, as praxis of differential inclusion, sets up the normative framework for the coexistence of various linguistic forms and resources, whether recognised officially or not, and their use in creative ways for pragmatic communication in completing daily tasks. In this context, the norms of metrolingual multitasking reveal an overall sense of ordinary coexistence in living with such diversity as a requirement for successful integration, despite necessary instances of differential treatment and exclusionary practices, including a refusal to engage with difference.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".