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Record W4416134567 · doi:10.1177/02697580251370767

Chainsaws and bombshells: Brutality and victim sexualization in slasher films, 1960–2019

2025· article· en· W4416134567 on OpenAlexaff
Michael Bachmann, Brittany A Bachmann, Ashley Wellman

Bibliographic record

VenueInternational Review of Victimology · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMagnus Chemicals (Canada)
Fundersnot available
KeywordsSexualizationPoison controlPerceptionHuman sexualitySuicide preventionRelation (database)Human factors and ergonomicsInterrogation

Abstract

fetched live from OpenAlex

This study examines the evolution of slasher films from the 1960s to the present, focusing on their portrayal of violence, brutality, and the sexualization of female victims in relation to shifting societal attitudes toward victimization. By conducting a data-driven content analysis of violence and sexualization trends through the decades, the research fills a gap in the literature on the intersection of cinema, victimology, and culture. The findings reveal that films from the 2000s and 2010s exhibit the highest levels of brutality, while 1980s slasher films are marked by the most explicit sexualization of female victims. These trends are analyzed through the lens of victimology, demonstrating how depictions of female victimization reflect and reinforce societal attitudes toward crime, serial killing, rape culture, and gender dynamics. The study highlights the reciprocal relationship between slasher films and cultural movements, illustrating how filmmakers both shape and are shaped by public perceptions of victimhood. Ultimately, the research argues that slasher films serve as a cultural mirror, revealing shifting views on gender, power, and crime, and offering insights into broader societal concerns about the victimization of women and changing notions of justice and empowerment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.387
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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