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Record W2002349087 · doi:10.1080/00224490902747727

Erotic Target Location Errors: An Underappreciated Paraphilic Dimension

2009· review· en· W2002349087 on OpenAlexaff
Anne A. Lawrence

Bibliographic record

VenueThe Journal of Sex Research · 2009
Typereview
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsTransvestismFetishismPsychologyDimension (graph theory)Psychoanalytic theorySex therapyValue (mathematics)PsychoanalysisParaphiliaSocial psychologySexual behaviorSociologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.301
GPT teacher head0.520
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations50
Published2009
Admission routes1
Has abstractyes

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