Communicating Science on, to, and with Racial Minorities during Pandemics
Bibliographic record
Abstract
Introduction Pandemics, such as Severe Acute Respiratory Syndrome (SARS) and COVID-19, can disproportionately impact migrants and racial minorities through increased morbidity and mortality (Pan et al, 2020) and limited consideration of their needs in measures to control disease spread (Tan, 2021). Furthermore, communication initiatives to educate the public about SARS and COVID-19, particularly their origin, spread, and control, can lead to stigmatisation, othering, and exclusion of Asian minorities (Hung, 2004). Although communicating the science associated with these infectious diseases can facilitate transparency and rationalise lockdown measures, they can also harm minority ethnic groups and the broader social fabric when conducted in a culturally insensitive and exclusionary manner. To illustrate the importance of sensitive and inclusive pandemic science communication, this chapter draws from accounts of two coronavirus pandemics. Before the 2020 COVID-19 pandemic, the world was threatened by SARS in 2003, especially the Canadian city of Toronto. Communication on its origin in China led to racially motivated attacks and discrimination against people who look East or South East Asian and against businesses in Toronto's Chinatowns (Keil and Ali, 2006). These forms of discrimination were also experienced by Australia's Asian minority population during the COVID-19 pandemic (Asian Australian Alliance and Chiu, 2020). However, the management of COVID-19 in Australia also highlighted the disproportionate impact of lockdown policies on racial minority and socio-economically disadvantaged groups, especially with inadequate communication of their implementation (Victorian Ombudsman, 2020) and scientific/epidemiological rationale (Patrick, 2021). These were further aggravated by limited engagement with community members in planning lockdowns (Victorian Ombudsman, 2020). This chapter draws from academic publications, reports, and news articles on SARS and COVID-19 to illustrate how different forms of communication on and during these pandemics profoundly affected the welfare of racial/ethnic minorities. Lessons from these incidents can be used to develop more inclusive ways of communicating pandemic science and formulating associated policies (Hyland-Wood et al, 2021). Experiences during SARS and COVID-19 can help develop pathways not just for communicating science involving racial minorities but also for relaying scientific information that has a profound impact on them. Going beyond communication on and to, lessons during these pandemics are vital in underscoring the importance of engaging with minorities to develop culturally sensitive communication strategies (Airhihenbuwa et al, 2020).
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".