The Double Standard of Stigma: Gender and Compulsive Sexual Behaviour
Bibliographic record
Abstract
There is strong evidence suggesting that addiction is highly stigmatized. However, less research has examined stigma associated with compulsive sexual behavior (CSB; i.e. sex addiction), and findings yield mixed results. Further, few studies have explored the role of gender in stigma associated with CSB, particularly within the context of problem pornography use (PPU). Therefore, we examined stigma associated with CSB – specifically PPU – compared to other addictions and mental health conditions, and determined if gender had an effect. Participants (N = 750, 53% women) were randomly assigned to read one of several vignettes each depicting a different addictive behavior (CSB, gambling, alcohol), mental health condition (depression) or health condition (cancer). There were two gendered versions (man vs. woman) of each vignette. Participants answered a series of questions to assess various types of stigma (e.g. general attitudes, affective reactions, social distance) associated with each condition. Results suggested that CSB was often more stigmatized than other conditions. Stigma toward men with CSB was greater than toward women with CSB. These findings suggest that gender may play a role in how individuals with CSB are perceived. Understanding stigma associated with CSB is essential given the associated negative consequences, including barriers to seeking treatment.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".