On the Social Existence of Mental Health Categories: The Case of Sex Addiction
Bibliographic record
Abstract
Abstract Mental health categories can circulate in societies regardless of whether they are recognized by medical professionals. This article asks why some labels are adopted en masse to commonly characterize some forms of distress, while other labels remain confined to specialist spheres. Contrasting with many examples of medicalization, “sex addiction” offers a heuristic case study because it was only after its exclusion from the Diagnostic and Statistical Manual of Mental Disorders (DSM) in 1994 that it became widely used to pathologize sexual excess in Western cultures. To understand how this and other categories acquire such popularity, it is necessary to account more explicitly for the multiple social appropriations of these categories within various non-medical fields and examine how they circulate between these fields. Drawing on two years of qualitative data collection from North American and Australian social institutions of non-medical therapy, law, the media, and religion, this article proposes a theoretical and methodological framework for studying the “social existence” of mental health categories such as sex addiction.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.038 | 0.106 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".