Do young female candidates face double barriers or an outgroup advantage? The case of the European Parliament
Bibliographic record
Abstract
Abstract While intersectionality is a recurrent theme in the literature on women's political representation, few studies empirically disentangle who are the women who get elected to parliaments. An argument on biases in recruitment practices suggests that those who are members of more than one outgroup, such as young women, benefit from an ‘outgroup advantage’. In elections, a candidate with two outgroup features might attract more voter support than a candidate with just one outgroup feature. Hence, nominating a candidate that is both young and female could be a rational move by (male) elites in political parties that allows them to open fewer seats to newcomers. These expectations are tested on data for all members of the European Parliament since 1979. Not only is it found that women's presence increased steadily throughout the parliament's history, but also that women's representation is consistently highest among the group of young representatives, lower among middle‐aged Members of the European Parliament and lowest among older representatives.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".