Revenge Porn: A Disturbing Trend in Sexual Violence That Must Be Exposed
Bibliographic record
Abstract
Revenge porn, also known as "pornodisclosure" is a form of online sexual violence that involves sharing sexually explicit images or videos of a person without their consent.This form of sexual violence has become increasingly prevalent in recent years due to the ease with which images can be shared on social networks and messaging applications.In this popularization article, we will begin by exploring the scope of revenge porn.Next, we will analyze the reasons that motivate perpetrators of this form of sexual violence and the psychological, social, economic, and occupational effects it has on victims.Finally, we will discuss the legal and ethical aspects of revenge porn and suggest preventive measures. A B S T R A C TLa vengeance pornographique, également connue sous le nom de « pornodivulgation » ou de « revenge porn », est une forme de violence sexuelle en ligne qui consiste à partager des images ou des vidéos sexuellement explicites d'une personne sans son consentement.Cette forme de violence sexuelle est devenue de plus en plus répandue ces dernières années en raison de la facilité avec laquelle les images peuvent être partagées sur les réseaux sociaux et les applications de messagerie.Dans le présent article de vulgarisation, nous débuterons en explorant la portée de la vengeance pornographique.Ensuite, nous analyserons les raisons qui motivent les auteurs de cette forme de violence sexuelle ainsi que les effets psychologiques, sociaux, économiques et professionnels qu'elle engendre chez les victimes.Enfin, nous aborderons les aspects légaux et éthiques liés à la vengeance pornographique et suggérerons des mesures préventives.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".