An Exploration of Animal Metaphors in Gender-Based Violence Campaigns
Bibliographic record
Abstract
This article explores the use of animal metaphors in gender-based violence campaigns. It analyzes a purpose-built corpus consisting of 39 public-awareness campaigns that run between 1998 to 2023 across 21 countries that resort to animal metaphors in the representation of the female victim and/or the male abuser. Through the lens of Conceptual Metaphor Theory, the results revealed that campaigns reproduce common zoomorphic tropes associated with existing misogynous and gender-based violence discourses, such as political, legal, mass, and social media, and popular culture. Male batterers are portrayed as aggressive, predatory, dangerous, and controlling through zoomorphic metaphors such as “animals”, “beasts”, “pit bulls”, “vultures”, “roosters”, “octopuses”, and “owls”. Female victims, on the other hand, are sexualized, subjugated, and derided, physically, intellectually, and morally, via comparisons with “bitches”, “vixens”, “cougars”, “cats”, “pigs” and “donkeys”.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".