A double-edged sword: the role of pornography in learning about BDSM
Bibliographic record
Abstract
Increasing depictions of kink in mainstream society are leading to a rise in people seeking to learn about and participate in bondage/discipline, domination/submission, and sadism/masochism (BDSM). For many, this initial education process is likely to focus on the Internet and pornography specifically. Existing research into the impact of pornography on sex education has been mixed, though predominantly negative. However, pornography’s role in BDSM education has yet to be explored. To address this gap, 18 semi-structured interviews were conducted with BDSM community leaders in Canada and the USA to examine from where and what practitioners learn about BDSM. Participants drew upon their personal experiences and those they interacted with in their capacity as a community leader. Interviewees identified the benefits and risks associated with using pornography as a learning tool for BDSM participation. They stressed how pornography’s value as a learning tool has evolved over time, how it can normalise BDSM and increase self-acceptance, teach skills, and reinforce negotiation and consent practices. However, if consumers cannot separate fantasy from reality, pornography can have negative effects. The impact of BDSM-themed pornography for education, especially in adolescents and young people, is discussed, as are directions for more in-depth investigations of this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".