Communicating Science on, to, and with Racial Minorities during Pandemics
Bibliographic record
Abstract
The marginalisation experienced by racial/ethnic minorities during pandemics such as COVID-19 and SARS illustrates the importance of diversity and inclusion in science communication and public health. With the first cases of these pandemics reported in East Asia, epidemiologic and medical communication associating them with China led to racially motivated abuse and discrimination against individuals of Asian descent and appearance. Moreover, limited linguistically diverse and culturally sensitive health communication exacerbated infection rates, disease morbidity and mortality, and knowledge gaps already experienced by various culturally and linguistically diverse populations. This chapter explores scientific and medical communication during SARS in Canada and COVID-19 in Australia, two highly multicultural countries with significant Asian minority populations. It draws upon academic literature, media articles, and government reports to illustrate biases and gaps in scientific communication towards Asians and other minority ethnicities during these pandemics, and underscores how these have compounded the vulnerabilities they are already experiencing as a result of underlying health disparities and racist attitudes. It then advances the importance of communicating science to and with ethnic/racial minorities during pandemics to ensure effective and equitable outbreak control measures, discourage discrimination, and reduce mistrust in public health systems and interventions. Although communicating in multiple languages and accounting for diverse contexts and cultures are crucial, science communication during pandemics should go further and embrace a bidirectional approach wherein members of racial and ethnic minorities are empowered to co-create and lead communication efforts towards the communities of which they are part. Through democratising health communication and empowering underrepresented minority populations, we can start to address not just health disparities experienced by racial minorities but also issues of inclusivity, diversity, and representation in science communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".