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Record W1965899219 · doi:10.1348/1355325041719338

Seeing things differently: The viewing time alternative to penile plethysmography

2004· article· en· W1965899219 on OpenAlexaffabout
D. Richard Laws, Carmen L.Z. Gress

Bibliographic record

VenueLegal and Criminological Psychology · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUnavailabilityComputer scienceUsabilitySet (abstract data type)Stimulus (psychology)AccidentalVirtual realityHuman–computer interactionPsychologyCognitive psychologyEngineeringAcoustics

Abstract

fetched live from OpenAlex

This paper describes the use of viewing time (VT), how long a subject looks at a probable sexual stimulus, as an alternative to penile plethysmography (PPG). We then trace the history of VT to assess sexual interest from 1942 to the present. The first computer‐generated stimulus set for PPG was developed by Canadian researchers. This set was tested using VT by the second author and found valid. The unavailability of the set for further use resulted in the authors developing a new, computermodified set with an alternate form version. This required the modification and compositing of images of real people. The proposed research using this set is described. To avoid using real photographic imagery in the future we propose use of specialized computer software that permits development of images from scratch. VT is a limited technology and we close with descriptions of new ways to assess sexual interest and behaviour using virtual reality and virtual environments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.321
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations110
Published2004
Admission routes2
Has abstractyes

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