Bibliographic record
Abstract
Abstract Enormous differences exist in rates of death from COVID-19 in countries around the world. Collectivist cultures and countries are characterized by concern for culture and country to a greater extent than for self-interest, whereas the reverse is true for individualistic cultures and countries. In light of this cultural difference, and suggestive evidence that cultures known for their collectivist orientation are more likely to have near-universal compliance with infection-preventive behaviors such as public mask-wearing and less likely to place their elderly in nursing homes (which account for a high proportion of deaths in individualistic countries such as the US, Canada, and the UK), we hypothesized that death per million (DPM) rates would be significantly lower for collectivist countries than individualistic countries. We categorized every country for which there are collectivist-individualistic scores and split the countries into two groups as defined by Hofstede’s (1980) cut-offs. As predicted, the DPM rate for collectivist countries was significantly lower than for individualistic countries. Furthermore, an analysis of covariance controlling for median age showed that the alternative explanation that the observed difference could be accounted for in terms of the significantly lower average age of citizens of collectivist countries was implausible. Implications in areas related to reopening schools, etc., and directions for future research 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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".