Teaching Paraphilias with the DSM 5: Learning the Distinction between Difference and Disorder
Bibliographic record
Abstract
Popular media, public discourse, and many clinicians are often unclear about the difference between paraphilias and paraphilic disorders. Education about paraphilias and diagnostic criteria is scarce in human sexuality education. Many clinicians are poorly educated about non-normative sexual interests. After a brief history about paraphilias and a background on the need for this information, we supply a lesson plan about paraphilias and paraphilic disorders using diagnostic criteria from the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM 5). Major concepts included in this lesson are the distinction between paraphilias and paraphilic disorders and the difference between having a paraphilia and acting on it. We offer a section for further reading as well as a resource about paraphilic disorders adapted from the DSM 5.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".