The World at 7:00: Comparing the Experience of Situations Across 20 Countries
Bibliographic record
Abstract
The purpose of this research is to quantitatively compare everyday situational experience around the world. Local collaborators recruited 5,447 members of college communities in 20 countries, who provided data via a Web site in 14 languages. Using the 89 items of the Riverside Situational Q-sort (RSQ), participants described the situation they experienced the previous evening at 7:00 p.m. Correlations among the average situational profiles of each country ranged from r = .73 to r = .95; the typical situation was described as largely pleasant. Most similar were the United States/Canada; least similar were South Korea/Denmark. Japan had the most homogenous situational experience; South Korea, the least. The 15 RSQ items varying the most across countries described relatively negative aspects of situational experience; the 15 least varying items were more positive. Further analyses correlated RSQ items with national scores on six value dimensions, the Big Five traits, economic output, and population. Individualism, Neuroticism, Openness, and Gross Domestic Product yielded more significant correlations than expected by chance. Psychological research traditionally has paid more attention to the assessment of persons than of situations, a discrepancy that extends to cross-cultural psychology. The present study demonstrates how cultures vary in situational experience in psychologically meaningful ways.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".