Variation in women's political representation: media's impact on how women are viewed in the political sphere
Bibliographic record
Abstract
Research shows women vote more than men but are under-represented in government positions. When looking at the countries the United States, Canada, and the United Kingdom one can find a variation in the representation of women in government positions. This variation in representation is interesting because they are countries with similar political, cultural, and socio-economic structures. There are some theories to explain why women are underrepresented in government, but these theories do not explain the variation between the representation of countries of similar structure. This study proposes that sexist media plays a role in this variation and impacts the rate at which women are elected to office. It surmised that Canada would have the least amount of sexist media and the U.S. would have the most. To compare the three countries' levels of sexism it looked at the amounts of modern sexism in the media. This study found a strong connection between sexism in the media and the variation in women’s representation in politics between the three countries. However, it did not find that sexism in the media is the strongest reason that impacts the chances of a female candidate being elected. It found that there were many other factors that play into a female candidate being elected. While sexist media representation did play a conclusive role in the variation between countries, more research needs to be done for a conclusive answer on how large of a role negative media plays in women losing or winning their elections.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".