Erotic Target Location Errors: An Underappreciated Paraphilic Dimension
Bibliographic record
Abstract
Based on studies of heterosexual male fetishists, transvestites, and transsexuals, Blanchard (1991) proposed the existence of a hitherto unrecognized paraphilic dimension, erotic target location errors (ETLEs), involving the erroneous location of erotic targets in the environment. ETLEs can involve preferential attention to a peripheral or inessential part of an erotic target, manifesting as fetishism, or mislocation of an erotic target in one's own body, manifesting as the desire to impersonate or become a facsimile of the erotic target (e.g., transvestism or transsexualism). Despite its potential clinical and heuristic value, the concept that ETLEs define a paraphilic dimension is underappreciated. This review summarizes the studies leading to the concept of ETLEs and describes how ETLEs are believed to manifest in men whose preferred erotic targets are women, children, men, amputees, plush animals, and real animals. This review also describes ETLEs in women; discusses possible etiologies of ETLEs; considers the implications of the ETLE concept for psychoanalytic theories of transvestism and male-to-female transsexualism, as well as for the forthcoming revision of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; suggests reasons why the concept of ETLEs has been underappreciated; and describes what might result if the concept were more widely appreciated.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".