Political Gender Stereotypes in a List-PR System with a High Share of Women MPs: Competent Men versus Leftist Women?
Bibliographic record
Abstract
On the basis of a candidate’s sex, voters ascribe particular personality traits, capacities, and opinions to candidates (often to the detriment of women), which are referred to as political gender stereotypes. The prevalence of political gender stereotypes has almost exclusively been investigated in the United States. As the presence of these stereotypes is highly dependent on contextual factors, we switch the context and investigate whether they are also present in a List-Proportional Representation (PR) system with a high share of women in parliament spread over different parties. The results of our experimental study, conducted in Flanders (Belgium), provide evidence for the existence of stereotypical patterns. The differences in perceived issue competence are, however, rather small and not always unequivocal, but larger differences were found in terms of ideological position. This leads us to conclude that misperceptions about women’s ideological orientation might be persistent and difficult to overcome. Moreover, our results demonstrate that the argument that female politicians are perceived as more leftist because they disproportionately belong to leftist parties does not hold, as female politicians are rather equally spread over the different parties in Belgium.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".