Navigating the Racial Landscape: Malay Youth Experiences of Education and Work in Singapore
Bibliographic record
Abstract
Scholars have noted the need for both empirical and theoretical research on the unique configurations of race and racism within Asia. This paper explores the racialized landscape encountered by Malay youth during their education and employment in the city-state of Singapore. We highlight the three unique building blocks which comprise the country’s racial landscape, namely (i) race is used as a naming device by the state; (ii) economic and social inequality along the lines of race exist alongside discourses of meritocracy and (iii) discussions of race which can be perceived as offensive are violations of local laws. Based on focus groups conducted with Malay youth on their experiences and memories of their education and employment, we highlight their perspectives on racial stratification. We explore Singapore’s racial landscape within which Malay youth are excluded from networks, silenced through discourses of harmonious multiculturalism, and excluded from Chinese-language-based corporate cultures which are predominant. Our findings suggest that challenging racial inequality in multicultural cities requires the dismantling of systemic systems of stratification. Our analysis contributes to understanding the unique configurations of race and racism in Asia and amongst Asians.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".