The Effects of Culture and Cultural Media on COVID-19 Community Response
Bibliographic record
Abstract
The side-by-side communities of Markham and Vaughan Ontario, situated directly north of Toronto have similar income profiles, housing types, education, and age demographics. Both cities share consistent policies as members of the Regional Municipality of York. Yet despite similarities, a year and a half into the COVID-19 pandemic, Vaughan saw 96% higher infection rates than Markham. One key difference in the demographics between Markham and Vaughan is the population of ethnically Chinese people. Markham has 77% visible minority and 45% ethnically Chinese population. Vaughan has an 8% ethnically Chinese population. The stark contrast in infection rates, despite similar socio-economic indicators, was the driving force behind this research. Key questions were whether culture or cultural influences played a role in pandemic behaviour. What role media consumed by diasporic communities had on decisions to wear a mask or practice social distancing, and how digital communication technologies can be used to influence pandemic behaviour. The experiences of Vaughan and Markham demonstrate a clear emphasis on economic issues in English-language Toronto-based media, which creates a bias of economic importance compared to Chinese language media, which focused on the health impacts of COVID-19. The coverage did not merely report on the pandemic; it mediated the response by its framing and messaging. Information flowed quickly from Chinese language sources to both individuals and family and friend group chats. The impact of this information flow was apparent as many Chinese Canadians adopted masking before mandates came into effect, in fact, many were masking while Canadian officials were asking people not to mask. Applied Science Communication, the science communication that attempts to influence either policy or public behaviour, should be treated differently from traditional forms of science communication. The political implications of behaviour compliance points to treating it more like political communication. The economic coverage of COVID-19 overshadowing the public health issues demonstrated how politically charged the issue became. As science and technology become more entwined in everyday life, scientists need to recognize the political implications of their work and research and communications should be done with this in mind.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".