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Record W4307261361 · doi:10.34778/5s

Consent Communication (Portrayals of Sexuality in Pornography)

2022· article· en· W4307261361 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 sexualityPsychologyCriminologyInternet privacySocial psychologySociologyGender studiesPsychoanalysisComputer science

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 consent communication 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 consent communication 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 depicts sex mostly without consent communication, especially explicit verbal communication. To test this hypothesis and code pornographic material accordingly, it is necessary to clarify the concept of “consent communication” and use valid and reliable measures for different types of consent communication. Here it is important to conceptually differentiate between consent communication between characters in the fictional world of the porn scene and consent communication between performers on set (or consent of performers to have their image be recorded and disseminated as pornography). This distinction becomes murky with regard to amateur pornography, which ostensibly is meant to depict “authentic” sex (although this sex may still be performative), and also professional pornography in which a performer is playing “themselves” as opposed to a character. Some production studios (especially those specializing in BDSM content) embed interviews with performers into their videos, in which performers indicate that they consented to the activities presented. Coding Material Measure Operationalization (excerpt) Reliability Source Consent Communication: Whenever in the sequence of sexual activities depicted in pornography a new activity is started, the question arises if all participants have consented to the new behavior. Sexual consent between characters can be communicated verbally and nonverbally (Willis et al., 2020). Further, verbal or nonverbal communication may be explicit or implicit (Willis et al., 2020). Apart from issues of performer health protection, explicit consent communication on camera is also regarded as relevant in terms of modelling behaviors for audiences. N=50 segments (length 20 min. each) from a random sample of 50 bestselling pornographic films (1 segment per film) depicting a total of 1,109 sexual behaviors Explicit verbal sexual consent “Straightforward statements, questions, or responses expressing agreement to engage in sexual behavior stated using words for actual sexual behavior or a very close synonym”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .72 Willis et al. (2020) Implicit verbal sexual consent “Verbally initiating sexual behavior or communicating agreement to engage in sexual behavior without explicitly using the word sex or other close synonyms. The content of the words may not be sexual in nature, but the connotation or tone of voice used by the characters implies sex or is sexual in nature”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .63 Explicit nonverbal sexual consent “Behaviors or actions that are sexually explicit including bodily touching in a sexual way”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .76 Implicit nonverbal sexual consent “Behaviors or actions that imply interest in engagement in sexual behavior”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .65 No response “Characters do not say anything, do not resist, or let the sexual activity happen without much action. The person is a passive participant in sexual behavior, but not uncomfortable, distressed, or showing signs of disinterest”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .72 No sexual consent shown “Scene begins or video cuts away and comes back with characters engaging in sexual behavior without any preceding actions to assess consent”. Binary coding (1: present; 2: not present). Cohen’s Kappa: .95 For more nuanced analyses of consent communication, the sex/gender of the persons involved in consent communication can be coded (see DOCA entry “Performer Demographics (Portrayals of Sexuality in Pornography)”) and the respective sex acts that are to be consented to (see DOCA entry “Sex Acts (Portrayals of Sexuality in Pornography)”). Furthermore, relationship status between characters might play an important role for consent communication (Willis et al., 2020): Characters in established relationships might be more likely to communicate sexual consent nonverbally than those in casual encounters (see DOCA entry "Relational Context of Sex (Portrayals of Sexuality in Pornography"). 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 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 perceptions of the likelihood of women engaging in porn-like sex. Psychology of Popular Media Culture, 8(4), 365–375. https://doi.org/10.1037/ppm0000202 Miller, D. J., Raggatt, P. T. F., & McBain, K. (2020). A literature review of studies into the prevalence and frequency of men’s pornography use. American Journal of Sexuality Education, 15(4), 502–529. https://doi.org/10.1080/15546128.2020.1831676 Paasonen, S. (2021). “We watch porn for the fucking, not for romantic tiptoeing”: Extremity, fantasy and women’s porn use. Porn Studies, 1–14. https://doi.org/10.1080/23268743.2021.1956366 Seehuus, M., Stanton, A. M., & Handy, A. B. (2019). On the content of "real-world" sexual fantasy: Results from an analysis of 250,000+ anonymous text-based er

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 score0.227
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.139
GPT teacher head0.380
Teacher spread0.241 · 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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Citations3
Published2022
Admission routes1
Has abstractyes

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