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Enregistrement W4307261182 · doi:10.34778/5p

Performer Bodily Appearance (Portrayals of Sexuality in Pornography)

2022· article· en· W4307261182 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 sexualityPerforming artsPsychologySocial psychologyAestheticsGender studiesArtSociologyPsychoanalysisVisual arts

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 performer bodily appearance 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 performer bodily appearance 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 performers in pornography are mainly selected and presented to conform to gendered norms of sexual attractiveness but also potentially unhealthy beauty standards or current beauty trends. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “performer bodily appearance” and use valid and reliable measures for different aspects of appearance. In addition, it is necessary to code the sex/gender of the persons depicted. Two different approaches to coding are available: Direct coding based on the performer’s appearance (e.g., breast size) versus indirect coding based on meta-information about the material, such as the sub-genre pornography category the material belongs to (e.g., the “big tits”, “BBW” [big beautiful women], “tattoed women” categories on PornHub) or statistics provided as part of performer profiles published on online platforms (e.g., height, weight, bra or penis size). Coding Material Measure Operationalization (excerpt) Reliability Source Performer Bodily Appearance: Among the many aspects of performer appearance, those conventionally related to sexual attractiveness are measured most often in the context of pornography research. Researchers may also measure variables related to general beauty trends in society (e.g., shaving of pubic and body hair) or assess aspects of performer appearance which could be consider to promote unhealthy/unrealistic beauty standards (e.g., performers being unhealthily underweight or extremely muscular). Apart from issues of performer health protection, unhealthy standards of beauty and sexual attractiveness are also regarded as relevant in terms of modelling behaviors for audiences. N=50 best-selling pornographic videos and DVDs in Australia in 2003 with 838 sexual scenes Performer body type Performer body type. Polytomous coding (1: unhealthy underweight; 2: slim / undertoned; 3: average (untoned); 4: average (toned); 5: bulked up / very muscular; 6: overweight). Not available McKee et al. (2008) Performer breast size Performer breast size. Polytomous coding (1: smaller than average breasts; 2: average-sized breasts; 3: larger than average breasts). Performer breast surgery Performer breast surgery is obvious. Polytomous coding (1: yes; 2: no; 3: unsure). Performer penis size Performer penis size. Polytomous coding (1: smaller than average penis; 2: average-sized penis; 3: larger than average penis). N > 6,900 performer profiles from 10 gay male adult websites Performer penis size Performer penis size (as listed in performer profile). Polytomous coding (1: 5–6.5 inches; 2: 7–8 inches, 3: 8.5–10 inches, 4: 10.5–13 inches) Not available Brennan (2018) N=50 MILF [“Mother I’d like to fuck” sub-genre category] and 50 “Teen” pornographic videos randomly selected from 10 different adult websites (10 videos per website) Performer pubic hair Performer pubic hair. Polytomous coding (1: none; 2. groomed; 3: natural). Percentage agreement across all variables in codebook: 90.3% Vannier et al. (2014) References Brennan, J. (2018). Size matters: Penis size and sexual position in gay porn profiles. Journal of Homosexuality, 65(7), 912-933. https://doi.org/10.1080/00918369.2017.1364568 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., Albury, K., & Lumby, C. (2008). The porn report. Melbourne University Press. 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 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 Vannier, S. A., Currie, A. B., & O'Sullivan, L. F. (2014). Schoolgirls and soccer moms: A content analysis of free “teen” and “MILF” online pornography. Journal of Sex Research, 51(3), 253-264. https://doi.org/10.1080/00224499.2013.829795 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 candidatesMéta-épidémiologie (sens strict), Charge 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 enseignants0,174
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
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 Opus0,082
Tête enseignante GPT0,349
Écart entre enseignants0,267 · 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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