Optimism across cultures: In response to the severe acute respiratory syndrome outbreak
Bibliographic record
Abstract
Based on our early research, we predicted that the Chinese may be more optimistic and less pessimistic than North Americans in response to negative life events. A survey was conducted to investigate optimism cross culturally in the context of the severe acute respiratory syndrome (SARS) outbreaks in Canada and China. Chinese students in Beijing and European Canadians in Toronto answered questions about their perceptions of SARS. No significant cultural difference was found on dispositional optimism, as measured by the Revised Life Orientation Test (LOT‐R). Unrealistic optimism was measured in the context of SARS. Both groups demonstrated unrealistic optimism (i.e. reporting that the self was less likely than an average person to get infected with SARS). Such optimistic bias was stronger among Chinese than among Canadians. Compared to the actual infection rates in Beijing and Toronto, both Chinese and Canadian participants overestimated their own chances of getting infected, indicating that they were being pessimistic. Indeed, Chinese were less pessimistic than Canadians. In addition, even though the Chinese reported more inconvenience brought by SARS than did Canadians, they also reported more positive changes brought by SARS, reflecting the Chinese dialectical views of events. Implications for research on optimism in context are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".