The Risk Perception of the Chinese Diaspora during the COVID-19 Pandemic: Targeting Cognitive Dissonance through Storytelling
Bibliographic record
Abstract
The global COVID-19 pandemic in 2020 required risk communications to mitigate the virus' spread. However, social media not only conveyed health information to minimize the contagion, but also distracted from the threat by linking it to an externalized 'other'-primarily those appearing to be of Chinese descent. This disinformation caused the attribution of blame to Chinese people worldwide. In Canada's Greater Toronto Area, Chinese individuals reported widespread public stigma that compounded their risk of contagion; to the degree that it was driven by cognitive dissonance, it generated experiences of social and cultural vulnerability. In this paper, we draw on the aforementioned study's findings to explain how the risk perception and threat appraisal of Chinese diaspora individuals were impacted by different cognitive dissonance pathways. These findings explore how storytelling is a viable intervention with which to target and mitigate cognitive dissonance. Indeed, the mechanisms of cognitive dissonance can modify risk perception and mitigate social and cultural vulnerability, thereby averting potential long-term negative consequences for one's mental health and well-being. We hope our guidance, training educators to target pathways of cognitive dissonance by drawing on storytelling (with humour), can assist them to better convey information in ways that are more inclusive during public health emergencies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".