COVID-19, economic threat and identity status: Stability and change in prejudice against Chinese people within the Canadian population
Bibliographic record
Abstract
Objectives: Previous studies found a general increase in prejudice against Chinese people during the first months of the pandemic. The present study aims to consider inter-individual heterogeneity in stability and change regarding prejudice involving Chinese people during the pandemic. The first objective is to identify and describe different trajectories of prejudice over a seven-month period during the pandemic. The second and third objectives are to test the association between trajectory group membership and antecedent variables such as: socio-demographic factors (i.e., age, gender, political affiliation) and two psychological mechanisms, namely economic threat and global citizenship identification. Methods: = 3,617) according to age, gender and province of residence, was recruited for a 10-wave survey starting from April 2020 to December 2020. First, a group-based modeling approach was used to identify trajectories of prejudice. Second, a multinomial logistic regression model was used to test associations between membership in trajectories and antecedents. Results: of Chinese people which become more positive throughout 2020. Regarding socio-demographic factors: gender is not associated with trajectory group membership, younger people are more likely to follow the trajectory in favor of Chinese people and conservatives are more likely to follow the highest trajectories against Chinese people. Regarding some psychological mechanisms: personal but not collective economic threat is associated with the trajectory in favor of Chinese people. Finally, the highest levels of prejudice are found when the strategy of identification is more local rather than global. Conclusion: The present study shows that Canadians differ in terms of both their level and change in prejudice against Chinese people throughout the pandemic with some socio-demographic groups being more likely than others to be associated with prejudice. The results also suggest that a promising way to tackle the major social issue of prejudice is to highlight a vision of the world where individuals are all "global citizens" facing the same challenge.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".