Wuhan residents perceptions of prejudice and discrimination and the social categorization processes during and after the COVID-19 lockdown: A qualitative study
Bibliographic record
Abstract
Although studies have focused on intergroup biases (e.g., discrimination) during the COVID-19 pandemic, little is known about the underlying mechanisms driving intergroup bias, specifically social categorization. In-depth interviews were conducted among 32 residents of Wuhan, P.R. China, during and after the initial COVID-19 lockdown (Jan-July, 2020). Interpretative phenomenological analysis (IPA) was applied to describe perceived prejudice and discrimination and the intergroup interaction processes. Results indicated that: (1) Wuhan residents’ interpretations of perceived prejudice and discrimination evolved over time, especially regarding views of the pandemic prevention measures; (2) intergroup contact, emotional factors and cognitive factors influenced individuals’ perceptions of prejudice and discrimination; (3) social categorization and integration processes underwent changes across different stages of the COVID-19 pandemic in Wuhan, encompassing the lockdown and reopening; (4) the group identity of recovered COVID-19 patients was easily solidified; (5) in the group integration process, emphasizing common attributes between groups, individualized media coverage and positive aspects of intergroup interactions weakened intergroup boundaries and promoted group integration. These results enrich existing knowledge about perceived discrimination and social categorization processes of a suddenly marginalized group through qualitative research methods.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".