Relational Context of Sex (Portrayals of Sexuality in Pornography)
Bibliographic record
Abstract
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 relational context of sex 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 relational context of sex 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: Common research hypotheses state that sex in pornography is mostly depicted as casual and/or extrarelational, even though real life sex predominantly occurs in committed relationships. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “relational context of sex” and use valid and reliable measures for different types of relational contexts. In addition, it is necessary to code the sex/gender of the persons involved. It is important to note that the relational context of sex may be determined based on the interactions and dialogue between performers or based on video titles and descriptions. For example, a video might depict sex with little or no dialogue indicative of the nature of the relationship between performers, but include a title or description that contextualizes this relationship (e.g., “Cheating wife has sex with stranger” or “woman surprises her fiancé”). Coding Material Measure Operationalization (excerpt) Reliability Source Relational Context of Sex: Two people engaging in sex (a dyad) can have different types of romantic or non-romantic relationships with each other and with further people outside this dyad. If a person is having sex with a person they just met, this is defined as casual sex; and if a person is in a monogamous relationship and engages in sex with another person outside this relationship, this is considered extrarelational sex or infidelity (Rasmussen et al., 2019). N=190 scenes (average length 14 min.) taken from the highest rated section of PornHub (86 scenes) and Xvideos (104 scenes) Casual sex and further relationship contexts with sex partners Relationship between dyad members for each dyad engaging in sex during the scene. Polytomous coding (0: no relational information; 1: just met / casual sex; 2: acquaintances/friends; 3: dating; 4: married; 5: not enough information). Krippendorff’s Alpha average of .74 for all variables in codebook Rasmussen et al. (2019) Extrarelational sex Sexual scene with at least one of the sexual participants being in a romantic relationship with someone not present in the sexual encounter. Binary coding (1: yes; 2: no). - Extrarelational participant dating (type of extrarelational sex) Sexual scene with at least one participant dating someone not present in the sexual encounter. Binary coding (1: yes; 2: no). - Extrarelational participant married (type of extrarelational sex) Sexual scene with at least one participant being married to someone not present in the sexual encounter. Binary coding (1: yes; 2: no). If sex is determined to be extrarelational it is possible to further code whether this extrarelational sex is happening with the knowledge, encouragement, or participation of the individual’s partner (e.g., as part of a cuckold fantasy). Rasmussen et al. (2019) refer to this as consensual non-monogamy. 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 Rasmussen, K. R., Millar, D., & Trenchuk, J. (2019). Relationships and infidelity in pornography: An analysis of pornography streaming websites. Sexuality & Culture, 23(2), 571–584. https://doi.org/10.1007/s12119-018-9574-7 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 erotic fantasies. Archives of Sexual Behavior, 48(3), 725–737. https://doi.org/10.1007/s10508-018-1334-0 Shor, E. (2022). Who seeks aggression in pornography? Findings from interviews with viewers. Archives of Sexual Behavior, 51(2), 1237–1255. https://doi.org/10.1007/s10508-021-02053-1 West, C. (2019). Pornography and ethics: An interview with porn performer Blath. Porn Studies, 6(2), 264–267. https://doi.org/10.1080/23268743.2018.1505540 Williams, L. (1989). Hard Core: Power, pleasure, and the frenzy of the visible. University of California Press.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".