What's wrong with cross-cultural comparisons of subjective Likert scales?: The reference-group effect.
Bibliographic record
Abstract
Social comparison theory maintains that people think about themselves compared with similar others. Those in one culture, then, compare themselves with different others and standards than do those in another culture, thus potentially confounding cross-cultural comparisons. A pilot study and Study 1 demonstrated the problematic nature of this reference-group effect: Whereas cultural experts agreed that East Asians are more collectivistic than North Americans, cross-cultural comparisons of trait and attitude measures failed to reveal such a pattern. Study 2 found that manipulating reference groups enhanced the expected cultural differences, and Study 3 revealed that people from different cultural backgrounds within the same country exhibited larger differences than did people from different countries. Cross-cultural comparisons using subjective Likert scales are compromised because of different reference groups. Possible solutions are discussed.
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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 | 0.204 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".