The Making of Monstrosity: Exploring the Monster Figure Through the Lens of Gender
Bibliographic record
Abstract
Abstract What relation does the monster figure have to gender? It is widely accepted that monsters in television, cinema and literature commonly stand in for the Other, be that a social, political or racialised Other. To consider monsters and monstrosity through the lens of gender is to investigate the links between the monster figure and the Others that exist under the system of patriarchy – most notably women, gender-diverse people and queer folks. In this collective chapter, Francesca Lopez, Russ Martin and Chloe Olivo explore how the monster figure relates to gender via a conversation that traces the links between three individually written chapters – X-Men: The Normative System Disguised as Mutant, Dragula and the Expansive Queerness of the Drag Supermonster and Femicide on the Frontier: Analysing Motives Behind the Femicide Crisis in Ciudad Juàrez. Each of these chapters investigates social norms relating to gender and those who challenge or defy them. Ultimately, the authors argue, it is those whose gendered and sexual identities are not associated with social power that are made monstrous by the patriarchy. This conversation-based chapter considers both real-life situations in which real people are made monstrous and monsters from fiction films and reality television. Ultimately, the authors suggest that the monster figure can be powerful and transformative for those who exist on the margins of the patriarchy – though, as this chapter documents, such is not always the case.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.076 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".