Increasing Women's Descriptive Representation, But Which Women? Analyzing the Impact of Gender Quotas in Post-Industrial Democracies
Bibliographic record
Abstract
The number of countries that have adopted gender quotas has increased dramatically over the past two decades with over one hundred countries now having some form of quota to increase women’s political representation. The increase in gender quotas has led to a corresponding increase in research examining this phenomenon; however, this research tends to examine what leads to successful quota adoption and whether quotas are successfully increasing the total number of women elected to political office. While these questions are undoubtedly important, arguments in favor of adopting quotas argue that quotas will not only increase the total number of women, but increase the diversity among the types of women elected to political office. This research seeks to go beyond the existing literature and examine what kinds of women are elected through quotas. In order to examine the kinds of women elected through quotas this research will analyze the profiles of men and women in the lower house of parliament in four post-industrial democracies: Australia, Canada, Germany, and the United Kingdom. The profiles of women members elected through quotas will be compared to women and men elected without the use of a quota to determine if quotas do change the kinds of women elected in terms of their age, race, government experience, occupation and education. Mapping these patterns can lead to a better understanding of whether and how quotas disrupt traditional candidate selection practices.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".