Item comparability in cross-national surveys: results from asking probing questions in cross-national web surveys about attitudes towards civil disobedience
Bibliographic record
Abstract
This article focuses on assessing item comparability in cross-national surveys by asking probing questions in Web surveys. The “civil disobedience” item from the “rights in a democracy” scale of the International Social Survey Program (ISSP) serves as a substantive case study. Identical Web surveys were fielded in Canada (English-speaking), Denmark, Germany, Hungary, Spain, and the U.S. A category-selection and a comprehension probe, respectively, were incorporated into the Web surveys after the closed-ended “civil disobedience” item. Responses to the category selection-probe reveal that notably in Germany, Hungary, and Spain the detachment of politicians from the people and their lack of responsiveness is deplored. Responses to the comprehension probe show that mainly in the U.S. and Canada violence and/or destruction are associated with civil disobedience. These results suggest reasons for the peculiar statistical results found for the “civil disobedience” item in the ISSP study. On the whole, Web probing proves to be a valuable tool for identifying interpretation differences and potential bias in cross-national survey 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 | 0.279 | 0.625 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".