Neutralizing Rape: Applying Sykes and Matza's Techniques to Rape Myths on <i>Law &amp; Order: Special Victims Unit (SVU)</i>

Gemini Alexis Creason‐Parker

Social Science Quarterly2026https://doi.org/10.1111/ssqu.70140article
ABDC B
Weight
0.50

What the paper says

ABSTRACT Objective This study applied Sykes and Matza's techniques of neutralization to examine how Law &amp; Order: Special Victims Unit ( SVU ) portrays and rationalizes sexual violence through rape myths in dialogue and narrative framing. Methods A qualitative content analysis was conducted on the first 10 seasons of SVU (225 episodes). A trained research team identified rape myth–related dialogue using validated rape‐myth acceptance (RMA) scales. A total of 183 segments were analyzed in NVivo and coded according to five neutralization techniques. Results All segments contained at least one neutralization technique, yielding 560 instances. Denial of victim, denial of responsibility, and denial of injury were most common. Appeals to higher loyalties and condemnation of the condemners were less frequent. Neutralizing language was used by offenders, victims, and justice system actors. Conclusion SVU frequently reproduced rape myths through neutralization strategies that normalize victim‐blaming and minimize offender responsibility. These findings extend neutralization theory and demonstrate the influence of fictional crime television on public understandings of sexual violence.

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https://doi.org/https://doi.org/10.1111/ssqu.70140

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@article{gemini2026,
  title        = {{Neutralizing Rape: Applying Sykes and Matza's Techniques to Rape Myths on <i>Law &amp; Order: Special Victims Unit (SVU)</i>}},
  author       = {Gemini Alexis Creason‐Parker},
  journal      = {Social Science Quarterly},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/ssqu.70140},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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