Stigmatising and Racialising COVID-19: Asian People’s Experience in New Zealand
Bibliographic record
Abstract
The Asian community - the second largest non-European ethnic community in New Zealand - plays an important role in combatting the COVID-19 pandemic, evidenced by their active advocation for border control and mass masking. Despite the long history of racial discrimination against the Asian population, the Asian community has experienced certain degrees of racial discrimination associated with the stigmatisation as the cause of the COVID-19 outbreak in New Zealand. Based on data from a quantitative online survey with 402 valid responses within the Asian communities across New Zealand and the in-depth interviews with 19 Asian people in Auckland, New Zealand, this paper will illustrate Asian people's experience of racial discrimination and stigmatisation during the pandemic in the country. The survey shows that since the outbreak of COVID-19, under a quarter of the participants reported experiencing discrimination, and a third reported knowing an immediate contact who had experienced discrimination. However, when looking beyond their immediate social circle, an even higher proportion reported noticing racism and stigmatisation through the traditional or social media due to COVID-19. Major variations of the degree of racial discrimination experienced are determined by three demographic variables: ethnicity, age, and region. The in-depth interviews largely echoed the survey findings and highlighted a strong correlation between the perceived racial discrimination among the local Asian community and the stigmatisation associated with COVID-19. These findings are important for improving the way we manage future pandemics and other disasters within the context of the UN Sendai Framework for Disaster Risk Reduction.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".