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Record W7067130287

Making our Monsters: Forced Disabling in American and Canadian Horror Films

2025· article· en· W7067130287 on OpenAlexaboutno aff

Bibliographic record

VenueThinkTech (Texas Tech University) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersTexas Tech University
KeywordsRacismOppressionWhite (mutation)Disability studiesRepresentation (politics)Race (biology)IndigenousMovie theaterAutonomyInclusion (mineral)
DOInot available

Abstract

fetched live from OpenAlex

My dissertation challenges the common perception that horror cinema merely vilifies disability, strictly by perpetuating harmful stereotypes of disabled individuals as monstrous. I contend that while horror films have consistently used characters’ physical, intellectual, and psychological disabilities to evoke fear and revulsion, revisiting the genre through the lens of disability studies reveals that portrayals of disability are not universally exploitative, but rather point to more complex historical intersections. The dissertation introduces and defines the concept of “forced disabling,” a phenomenon where individuals are deliberately disabled (mentally, emotionally, or physically) for the benefit of others, as distinct from disabilities that arise naturally or through accidents. The dissertation examines how the genre uses disability as a form of Othering and draws on trauma studies, Indigenous studies, and race and gender studies to uncover how this trope intersects with historical systems of oppression and targeted racial discrimination. These include, in particular, the legacies of anti-Black racism through practices such as exploitative medical experimentation, police brutality, and forced sterilization, as well as anti-Indigenous racism through tactics like cultural erasure, enforced dependency, and physical and psychological violence within settler-colonial frameworks. By comparing films directed by white male filmmakers with those by women and people of color, the study traces evolving trends in disability representation across time and cultures. Chapters include analyses of ways that concepts of race and gender inform the forced disabling of white protagonists in The Shining (Stanley Kubrick, 1980) and Misery (Rob Reiner, 1990) the ways female directors explore bodily autonomy through “body horror” in films such as American Mary (the Soska sisters, 2012) and Boxing Helena (Jennifer Lynch, 1993), and the way the historical institutions of slavery and Indigenous genocide manifest as forced disabling in the films of Black filmmakers including Jordan Peele’s Get Out (2017), and Indigenous filmmakers including Jeff Barnaby’s (Mi’kmaq) Rhymes for Young Ghouls (2013). My dissertation offers a comprehensive understanding of how forced disabling operates within horror and highlights its cultural significance and sheds light on how filmmakers from diverse backgrounds engage with themes of trauma and disability, all of which provides a more inclusive perspective within the genre. Ultimately, my research demonstrates that, rather than universally denigrating disability, horror uses it to reflect and critique deeply rooted societal injustices. Through a blend of disability studies, trauma theory, and intersectionality, my research reveals the multilayered ways in which horror uses disability to reflect and critique historical and societal injustices.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0300.011
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.014
GPT teacher head0.261
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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