The discursive construction of self-diagnosed “pornography addiction”
Bibliographic record
Abstract
Addiction is a term recently extended to problem sexual behaviours. Proponents of pornography addiction (PA) argue that pornography is comparable to drugs in its impact on brain processes and behaviour, producing effects similar to substance abuse disorders. Critics, however, assert that PA lacks diagnostic validity, that supporting research is methodologically unsound, and that diagnosis obscures the social contexts and discursive practices in which sexual behaviours are embedded. This study investigates how self-identified pornography addicts describe their experiences and explores the implicit motives and meanings at play in this identity construction. Using a psychoanalytically informed discursive methodology, the authors analyzed interviews with 10 self-identified pornography addicts, focusing on the meaning of self-diagnosis, the process of self-labelling, and participants’ psychological investment in diagnosis. Participants perceived an enslavement to desire as central to their addiction identities. Underlying this identity work were defences and conflicts about power, gender, sexuality, and perversion, as well as histories of disturbed attachment and deprivation by parental figures. The PA diagnosis counteracted feelings of shame and allowed participants to speak more freely about their difficulties. However, it also precluded more nuanced self-understandings and identity possibilities. The article concludes with a discussion of the mental health and psychotherapeutic implications of those presenting with self-diagnosed PA.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".