Testing for comparability of human values across countries and time with the third round of the European Social Survey
Bibliographic record
Abstract
This study tests the compatibility and comparability of the human values measurements from the third round of the European Social Survey (ESS) to measure the 10 values from Schwartz’s (1992) value theory in 25 countries. Furthermore, it explains the dangers associated with ignoring non-invariance before comparing the values across nations or over time, and specifically describes how invariance may be tested. After initially determining how many values can be identified for each country separately, the comparability of value measurements across countries is assessed using multigroup confirmatory factor analysis (MGCFA). This is necessary to allow later comparisons of values’ correlates and means across countries. Finally, invariance of values over time (2002-07) is tested. Such invariance allows estimating aggregate value change and comparing it across countries meaningfully. In line with past results, only four to seven values can be identified in each country. Analyses reveal that the ESS value measurements are not suitable for measuring the 10 values; therefore, some adjacent values are unified. Furthermore, a subset of eight countries displays metric invariance for seven values, and metric invariance for six values is found for 21 countries. This finding indicates that values in these countries have similar meanings, and their correlates may be compared but not their means. Finally, temporal scalar invariance is evidenced within countries and over time thus allowing longitudinal value change to be studied in all the participating countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".