Bibliographic record
Abstract
Public opinion surveys are a fundamental tool to measure support for women's political rights. This article focuses on perceptions of women's suitability for leadership. To what extent do influential cross-country surveys that include such items suffer from measurement errors stemming from gender of interviewer effects? Building on the literature on social desirability, we expect that respondents are more likely to express preference for men's suitability as political leaders with male interviewers and more likely to state support for women's leadership when interviewed by a woman. We hypothesize that these processes are conditioned by having one's spouse present, by age differences between respondents and interviewers, as well as by respondents' levels of education. Analyzing Afrobarometer data, we generally find support for our claims. In addition, it seems that men are slightly more affected by such effects than women are. These gender of interviewer effects persist when analyzing alternative survey rounds and are insensitive to various fixed effects specifications and robustness tests. For the analysis of survey data, we suggest that researchers using gender-related items should control for gender of interviewer effects. We propose that comparative survey programs pay even more attention to interviewer characteristics and the interview situation in their protocols.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".