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Record W4307171291 · doi:10.34778/5n

Sex Acts (Portrayals of Sexuality in Pornography)

2022· article· en· W4307171291 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 sexualityPsychologyGender studiesArtSociologyPsychoanalysis

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 sex acts 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 sex acts as one dimension of 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 pornography depicts sexuality as exaggerated regarding the variety of depicted sex acts, including commonly depicting statistically uncommon acts. More specifically, it is hypothesized, that the typical heterosexual porn script (which often includes oral, vaginal, and anal intercourse altogether in one scene) might normalize, or even prescribe, engagement in oral and anal intercourse in everyday heterosexual encounters. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “sex acts” and use valid and reliable measures for different types of sex acts. In addition, it is necessary to code the sex/gender of the person depicted as involved in the respective sex acts in different roles (e.g., giving or receiving oral sex). It is important to note that in the context of pornographic content research, researchers conceptualize sex acts differently. In particular, some researchers categorize some sex acts as violence or degradation, while other researchers cover them as more or less common sexual practices (e.g., “hair pulling” can be understood and coded as violence or as an element of consensual rough sex practices; “name calling” can be understood as verbal aggression or degradation or as an element of consensual dirty talk practices; see DOCA entries “Violence (Portrayals of Sexuality in Pornography)” and “Degradation (Portrayals of Sexuality in Pornography)”). Coding Material Measure Operationalization (excerpt) Reliability Source Sex Acts: Various types of sex acts can be differentiated such as oral sex, spanking or ejaculating on the body (Carrotte et al., 2020). Usually, in pornography research, sex acts related to rough sex and some types of BDSM are categorized as “Violence” (see DOCA entry “Violence (Portrayals of Sexuality in Pornography)”) and sex acts related to paraphilias such as fetishes, kinks and some types of BDSM are categorized as “Degradation” (see DOCA entry “Degradation (Portrayals of Sexuality in Pornography)”). Respective categorizations are based on some observers’ moral evaluations and disregard consent and the pleasure of participants (or that of other observers). Hence, depending on the researcher’s perspective, the full spectrum of consensual sexual activities can be subsumed under “sex acts” or only a sub-set of sexual activities that are regarded as normative and normophilic (Miller & McBain, 2022; Zhou et al., 2019). N=3,053 pornographic videos randomly selected from Xvideos.com Kissing Percentage agreement average across all variables in codebook: 98% Zhou et al. (2019) - Light kissing Light kissing between actors on mouth. Binary coding (1: present; 2: not present). - Deep kissing Deep kissing between actors on mouth. Binary coding (1: present; 2: not present). - Kissing and sucking on body Light and/or deep kissing between actors on mouth and sucking on the other actor’s body. Binary coding (1: present; 2: not present). Manual / digital sexual stimulation - Manual stimulation of penis (type of manual/digital stimulation) Manual stimulation of penis. Binary coding (1: present; 2: not present). - Manual stimulation of vulva and/or vagina (type of manual/digital stimulation) Manual stimulation of vulva and/or vagina. Binary coding (1: present; 2: not present). - Manual stimulation of anus (type of manual/digital stimulation) Manual stimulation of anus. Binary coding (1: present; 2: not present). Oral Sex - Fellatio (type of oral sex) Oral-penile contact between actors. Binary coding (1: present; 2: not present). - Cunnilingus (type of oral sex) Oral-vulva or oral-vaginal contact between actors. Binary coding (1: present; 2: not present). - Anilingus (type of oral sex) Oral-anal contact (a.k.a. rimming) between actors. Binary coding (1: present; 2: not present). Intercourse - Vaginal intercourse (type of intercourse) Penetration of one actor’s vagina by another actor’s penis. Binary coding (1: present; 2: not present). - Anal intercourse (type of intercourse) Penetration of one actor’s anus by another actor’s penis. Binary coding (1: present; 2: not present). N=50 popular pornographic videos from PornHub.com Orgasm - Female orgasm Overt orgasm of female performer, as indicated by the presence of “squirting” or other verbal and nonverbal cues (e.g., facial contortions, moaning, verbal statements communicating orgasm). Binary coding (1: present; 2: not present). Percentage agreement: 92% Séguin et al. (2018) - Male orgasm Overt orgasm of male performer, as indicated by the presence of ejaculate or other verbal and nonverbal cues (e.g., facial contortions, moaning, verbal statements communicating orgasm). Binary coding (1: present; 2: not present). Percentage agreement: 100% The selected sex act variables can be complemented with further variables that go into more detail. For example, for many sex act variables it makes sense to differentiate between the passive/receiving and active/giving role of the performers involved (e.g., receiving oral sex or giving oral sex). Furthermore, in addition to the act of vaginal or anal intercourse different intercourse positions (e.g., lying, sitting, standing positions; woman on top or bottom during intercourse) could be coded. For a discussion of measurement problems and best practice regarding coding female orgasms see Lebedíková (2022).ReferencesCarrotte, 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/10.2196/16702Cowan, 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/10.1080/00224499409551726Döring, 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.2009.04.003Hardy, 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.2021-0047Keft-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.935Lebedíková, M. (2022). How much screaming is an orgasm: The problem with coding female climax. Porn Studies, 9(2), 208–223. https://doi.org/10.1080/

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.367
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.099
GPT teacher head0.360
Teacher spread0.261 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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