Factors associated with the perception of resentment towards the Chinese in Latin America during the first wave of the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Resentment towards the Chinese population was perceived during the first months of the pandemic because the pandemic/disease started in that country. OBJECTIVE: To determine the factors associated with the perception of resentment towards the Chinese in Latin America during the first wave of the COVID-19 pandemic. METHODOLOGY: Analytical cross-sectional study conducted during the second semester of the pandemic in more than a dozen countries. Four questions were asked about the perception of resentment towards the Chinese (Cronbach's Alpha: 0.88); those with the highest scores on the sum of the four questions were considered to have "more resentment towards the Chinese," and descriptive and analytical statistics were obtained. RESULTS: Of the 7721 respondents, in the multivariate analysis, it was found that there was a difference according to the country; compared to Peru, those who had more resentment towards the Chinese were those residing in Paraguay (aPR: 1.29; 95%CI: 1.17-1.42; p-value < 0.001) and Bolivia (aPR: 1.52; 95%CI: 1.37-1.68; p-value < 0.001), while Chile (aPR: 0.78; 95%CI: 0.69-0.88; p-value < 0.001) had less resentment: 0.69-0.88; p-value < 0.001), Mexico (aPR: 0.68; 95%CI: 0.57-0.80; p-value < 0.001), Panama (aPR: 0.71; 95%CI: 0.59-0.86; p-value < 0.001) and Costa Rica (aPR: 0.64; 95%CI: 0.49-0.85; p-value = 0.002). DISCUSSION: There was a significant difference in resentment for each country.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".