Validation of the Expression Regulation Scale (ERS) for display rule norms in four scenarios across four countries
Bibliographic record
Abstract
Human emotional communication relies on a shared understanding of the “display rule” norms that guide expression regulation. For example, a norm to show happiness at gifts, irrespective of how one feels. However, these norms vary across cultures, which may hinder emotional communication. The present study tests the ability of a new tool—the Expression Regulation Scale (ERS)—to measure display rules across four English-speaking countries, including Western (US, Canada, Australia) and Asian (Singapore) cultures. Participants (N = 2139; with n > 500 per country) were recruited using quota sampling to have similar age and gender distributions and match each country’s ethnicity distribution, ensuring results generalise to the country’s population. Results show the three-factor structure of the ERS was a good fit for the data in all four countries across four scenarios, combining public and private contexts with close and distant interaction partners. Critically, the scale also showed measurement invariance across the countries and scenarios, which means that display rule scores can be validly compared. Cronbach’s alpha exceeded .9 in every instance, reflecting excellent reliability. Overall, when considered in combination with previous data from the UK (Dawel, Ashhurst, et al., 2023; Monaghan et al., 2023), the present study shows that the three-dimensional structure of display rules is robust and provides a useful avenue for comparing complex emotional structures across different social situations and cultures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".