Value Change in Europe and North America: Convergence or Something Else?
Bibliographic record
Abstract
Introduction A large body of empirical evidence demonstrates that the basic values of mass publics in advanced industrial societies have changed over the last three decades. The same research also shows that there are significant and persistent crossnational differences in values. This chapter considers whether the trajectory and pace of value change in advanced industrial countries is leading to convergence or divergence in the values of publics in Europe and North America. The question of value convergence or divergence can be conceptualized and addressed empirically in at least two ways. The most straightforward approach entails identifying common value domains among European and North American publics and then asking, Have these become more, or less, alike over the two decades for which we have data? A second approach, however, is to explore the internal dynamics of value change by examining how North American and European publics organize their core values. After outlining some different perspectives on value change and describing our data and methodological approach, we present the basic crossnational and crosstime evidence of change on single-value dimensions for publics in Europe and North America. The focus then shifts to consider the matter of how publics on both continents bundle their basic value outlooks. Do the publics in North America and Europe organize their basic value outlooks in similar or different ways? And are there discernible patterns in the way in which these core values have changed over the same period?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".