She’s Such a Bitch! The Representation of Women as Bitches in Gender-Based Violence Campaigns
Bibliographic record
Abstract
This paper examines the representation of women as «bitches» in gender-based violence campaigns. It analyzes a purpose-built corpus consisting of 22 campaigns from 17 countries dated from 1999 to 2023 that represent women as «bitches». Many of these campaigns have been financed by general and state governments; others, by non-governmental organizations or associations in defense of children. Some campaigns have been created on the internet; others are based on the lyrics of popular songs and even on street graffiti. Some take the form of posters, television and radio commercials and even documentaries. They constitute, therefore, a wide and diverse repertoire of gender-based violence campaigns. The project considers the linguistic, visual and acoustic representations of women as bitches given that several campaigns juxtapose photographs of battered women and real female dogs, characterize women as literal bitches by portraying them kneeling and with a leash around their necks held by a man and evoke the canine image through word play and onomatopoeia. The study employs the metaphor identification procedure for the spotting and coding of the metaphoric «bitch». Through the lens of Conceptual Metaphor Theory, the paper shows that gender-based violence campaigns resort to «bitch» to illustrate how this commonplace slur contributes to the dehumanization, objectification, sexualization and belittlement of women. It also shows that, despite the cultural and linguistic differences of the countries where the campaigns have been produced (Spain, Mexico, Colombia, Peru, China, the UK, Canada, the USA, Australia, France, Lebanon, Italy, Norway, Denmark, etc.), «bitch» is at the core of gender-based violence. The article, ultimately, demonstrates the close link between «bitch» and (the language of) gender-based violence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".