A Twenty-First Century Assessment of Values Across the Global
Bibliographic record
Abstract
This article provides current Schwartz Values Survey (SVS) data from samples of business managers and professionals across 50 societies that are culturally and socioeconomically diverse. We report the society scores for SVS values dimensions for both individual- and societallevel analyses. At the individual-level, we report on the ten circumplex values sub-dimensions and two sets of values dimensions (collectivism and individualism; openness to change, conservation, self-enhancement, and self- transcendence). At the societal-level, we report on the values dimensions of embeddedness, hierarchy, mastery, affective autonomy, intellectual autonomy, egalitarianism, and harmony. For each society, we report the Cronbach’s a statistics for each values dimension scale to assess their internal consistency (reliability) as well as report interrater agreement (IRA) analyses to assess the acceptability of using aggregated individual level values scores to represent country values. We also examined whether societal development level is related to systematic variation in the measurement and importance of values. Thus, the contributions of our evaluation of the SVS values dimensions are two-fold. First, we identify the SVS dimensions that have cross-culturally internally reliable structures and withinsociety agreement for business professionals. Second, we report the society cultural values scores developed from the twenty-first century data that can be used as macro-level predictors in multilevel and single-level international business research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".