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Record W4307171304 · doi:10.34778/5m

Degradation (Portrayals of Sexuality in Pornography)

2022· article· en· W4307171304 on OpenAlexaboutno aff
Nicola Döring, D.J. Miller

Bibliographic record

VenueDOCA - Database of Variables for Content Analysis · 2022
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPornographyHuman sexualityDegradation (telecommunications)PsychologyArtGender studiesSociologyPsychoanalysisComputer scienceTelecommunications

Abstract

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Pornography is a fictional media genre that depicts sexual fantasies and explicitly presents naked bodies and sexual activities for the purpose of sexual arousal (Williams, 1989; McKee et al., 2020). Regarding media ethics and media effects, pornography has traditionally been viewed as highly problematic. Pornographic material has been accused of portraying sexuality in unhealthy, morally questionable and often sexist ways, thereby harming performers, audiences, and society at large. In the age of the Internet, pornography has become more diverse, accessible, and widespread than ever (Döring, 2009; Miller et al., 2020). Consequently, the depiction of sexuality in pornography is the focus of a growing number of content analyses of both mass media (e.g., erotic and pornographic novels and movies) and social media (e.g., erotic and pornographic stories, photos and videos shared via online platforms). Typically, pornography’s portrayals of sexuality are examined by measuring the prevalence and frequency of sexual practices or relational dynamics and related gender roles via quantitative content analysis (for research reviews see Carrotte et al., 2020; Miller & McBain, 2022). This entry focuses on the representation of degradation as one of eight important dimensions of the portrayals of sexuality in pornography. Field of application/theoretical foundation: In the field of pornographic media content research, different theories are used, mainly 1) general media effects theories, 2) sexual media effects theories, 3) gender role, feminist and queer theories, 4) sexual fantasy and desire theories, and different 5) mold theories versus mirror theories. The DOCA entry “Conceptual Overview (Portrayals of Sexuality in Pornography)” introduces all these theories and explains their application to pornography. The respective theories are applicable to the analysis of the depiction of degradation as one dimension of the portrayals of sexuality in pornography. References/combination with other methods of data collection: Manual quantitative content analyses of pornographic material can be combined with qualitative (e.g., Keft-Kennedy, 2008) as well as computational (e.g., Seehuus et al., 2019) content analyses. Furthermore, content analyses can be complemented with qualitative interviews and quantitative surveys to investigate perceptions and evaluations of the portrayals of sexuality in pornography among pornography’s creators and performers (e.g., West, 2019) and audiences (e.g., Cowan & Dunn, 1994; Hardy et al., 2022; Paasoonen, 2021; Shor, 2022). Additionally, experimental studies are helpful to measure directly how different dimensions of pornographic portrayals of sexuality are perceived and evaluated by recipients, and if and how these portrayals can affect audiences’ sexuality-related thoughts, feelings, and behaviors (e.g., Kohut & Fisher, 2013; Miller et al., 2019). Example studies for manual quantitative content analyses: A common research hypothesis states that pornography often depicts sexuality which is degrading towards women (by men). (Conversely, an indicator against degradation is the depiction of sexual agency of women, i.e., representations of women actively initiating and guiding sexual encounters, and enjoying self-determined and reciprocal sex acts.) To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “degradation” and use valid and reliable measures of different types of degradation. In addition, it is necessary to code the sex/gender of the person depicted as the source and/or the target of the respective degrading act. It is important to note that in the context of pornographic content research, researchers conceptualize degradation differently. Also, it should be noted, that there is some overlap between the variable degradation and the variable violence in the context of pornographic portrayals of sexuality. For example, the depiction of “name calling” in a pornographic scene can be understood as an indicator of “violence” (namely verbal aggression) or of “degradation”. Name calling is covered as verbal aggression (following Fritz et al., 2020; see DOCA entry “Violence (Portrayals of Sexuality in Pornography)”), hence, it is not covered here again as degradation, even though some authors do so (such as Gorman et al., 2010). In general, one can argue that all violent acts – apart from being potentially painful and harmful – have a component of degradation because they put the target of violence in a subordinate role. However, not all degrading acts are violent (e.g., degradation by systematic lack of sexual reciprocity does not entail overt aggression). Coding Material Measure Operationalization (excerpt) Reliability Source Degradation: Degradation in the context of pornography is defined as a depiction of sexuality that is not characterized by mutuality, respect and equal power but instead is characterized by non-reciprocity, inequality, dominance, objectification and dehumanization, usually with men in the superior role and women in the subordinate role (Cowan & Dunn, 1994). Several variables indicating degradation during sex have been developed and are measured together with the sex/gender of persons involved, such as unreciprocated sex (e.g., female performer gives oral sex but does not receive it; male performer orgasms but female performer does not), status inequality (e.g., male performer depicted as older, better educated, more affluent than female performer), expressions of dominance (e.g., male performer ties female performer up or orders her around), objectification (e.g., male performer ejaculates on female performer’s body or face; gaping of the vagina or anus; double penetration of vagina or anus of the female performer) and dehumanization (e.g., male performer urinates on female performer’s body). While consensus can be reached between some researchers and media users that respective sex acts appear degrading to them (Cowan & Dunn, 1994), others disagree and either do not find these acts inherently degrading or recognize that they may be part of sexual fantasies and role play of degradation (Miller & McBain, 2022). Apart from issues of performer health protection, degrading acts are also regarded as relevant in terms of modelling behaviors for audiences. N=45 pornographic videos from 15 different adult websites (3 videos per website) Display of body Being degraded: Actor displayed showing a higher level of nudity in comparison to co-actor(s). Binary coding (1: yes; 2: no). Percentage agreement 100% for all degradation variables in codebook Gorman et al. (2010) Domination Degrading another: Actor displayed showing control and being in the dominating position, i.e. directing the co-actor(s) and the sexual acts. Binary coding (1: yes; 2: no). Submission Being degraded: Actor displayed in the submissive role, i.e. following demands, allowing to be moved in any position. Binary coding (1: yes; 2: no). Ejaculation onto the face Being degraded: Actor’s face or mouth displayed as being ejaculated on. Binary coding (1: yes; 2: no). Exploitation Degrading another: Actor displayed as using another with less power (e.g., due to age, social status, social role) as sexual object. Binary coding (1: yes; 2: no). Lack of reciprocity Degrading another: Actor displayed as disregarding mutuality and reciprocity during sexual acts and focusing only on their own satisfaction. Binary coding (1: yes; 2: no). References Carrotte, E. R., Davis, A. C., & Lim, M. S. (2020). Sexual behaviors and violence in pornography: Systematic review and narrative synthesis of video content analyses. Journal of Medical Internet Research, 22(5), Article e16702. https://doi.org/10.2196/16702 Cowan, G., & Dunn, K. F. (1994). What themes in pornography lead to perceptions of the degradation of women? Journal of Sex Research, 31(1), 11–21. https://doi.org/10.1080/00224499409551726 Döring, N. (2009). The Internet’s impact on sexuality: A critical review of 15 years of research. Computers in Human Behavior, 25(5), 1089–1101. https://doi.org/10.1016/j.chb.2009.04.003 Fritz, N., Malic, V. [Vinny], Paul, B., & Zhou, Y. (2020). A descriptive analysis of the types, targets, and relative frequency of aggression in mainstream pornography. Archives of Sexual Behavior, 49(8), 3041–3053. https://doi.org/10.1007/s10508-020-01773-0 Gorman, S., Monk-Turner, E., & Fish, J. N. (2010). Free adult internet web sites: How prevalent are degrading acts? Gender Issues, 27(3-4), 131–145. https://doi.org/10.1007/s12147-010-9095-7 Hardy, J., Kukkonen, T., & Milhausen, R. (2022). Examining sexually explicit material use in adults over the age of 65 years. The Canadian Journal of Human Sexuality, 31(1), 117–129. https://doi.org/10.3138/cjhs.2021-0047 Keft-Kennedy, V. (2008). Fantasising masculinity in Buffyverse slash fiction: Sexuality, violence, and the vampire. Nordic Journal of English Studies, 7(1), 49–80. Kohut, T., & Fisher, W. A. (2013). The impact of brief exposure to sexually explicit video clips on partnered female clitoral self-stimulation, orgasm and sexual satisfaction. The Canadian Journal of Human Sexuality, 22(1), 40–50. https://doi.org/10.3138/cjhs.935 McKee, A., Byron, P., Litsou, K., & Ingham, R. (2020). An interdisciplinary definition of pornography: Results from a global Delphi panel. Archives of Sexual Behavior, 49(3), 1085–1091. https://doi.org/10.1007/s10508-019-01554-4 Miller, D. J., & McBain, K. A. (2022). The content of contemporary, mainstream pornography: A literature review of content analytic studies. American Journal of Sexuality Education, 17(2), 219–256. https://doi.org/10.1080/15546128.2021.2019648 Miller, D. J., McBain, K. A., & Raggatt, P. T. F. (2019). An experimental investigation into pornography’s effect on men’s percept

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.357
Teacher spread0.257 · 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.

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".

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Published2022
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Same venueDOCA - Database of Variables for Content AnalysisSame topicSexuality, Behavior, and TechnologyFrench-language works237,207