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Enregistrement W4307264871 · doi:10.34778/5r

Relational Context of Sex (Portrayals of Sexuality in Pornography)

2022· article· en· W4307264871 sur OpenAlexaboutno aff
Nicola Döring, D.J. Miller

Notice bibliographique

RevueDOCA - Database of Variables for Content Analysis · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueSexuality, Behavior, and Technology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPornographyHuman sexualityContext (archaeology)PsychologyGender studiesSocial psychologySociologyPsychoanalysisHistory

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,227
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,117
Tête enseignante GPT0,350
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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