Violence against Women in Politics: An Urgent Problem the Political Science Community Must Take Seriously
Bibliographic record
Abstract
Violence against women in politics (VAWIP) is an urgent problem worldwide.At the time of this writing, U.S. House of Representatives Speaker Nancy Pelosi's husband had just been violently assaulted by a conspiracy theorist, shouting "Where is Nancy?" after breaking into their house.In Canada, women, Indigenous, Black, racialized, and queer political actors face harassment and threats on a regular basis.During the 2022 Québec provincial election, politician Marwah Rizqy received death threats from a man who allegedly called the police to inform them where they could find her body (she was pregnant at the time).In 2022, federal Deputy Prime Minister Chrystia Freeland was accosted by a man who yelled profanities at her while she was with her all-women staff.These are not isolated incidents, and the political science community has an important role to play in addressing them. What Can Political Scientists Do to Address Violence against Women in Politics?Research on VAWIP is developing, but gaps remain. 1 The first academic book written on this topic was Krook's Violence against Women in Politics, published by Oxford University Press in 2020-just three years ago.Prior to this, formative contributions include Piscopo (2016); Krook and Restrepo Sanín (2016);and Bardall, Bjarnegård, and Piscopo (2020).This scholarship builds on important contributions from global women practitioners, particularly in Latin America.The first gap is evidence based.More data are needed to document the prevalence and scope of VAWIP in all parts of the world.Replication of data findings and the development of shared concepts to better understand and create tools to address VAWIP are ongoing.Bjarnegård and Zetterberg's (2023)
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".