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Record W2132964674 · doi:10.2190/ic.32.2.d

Dreams with Sexual Imagery: Gender Differences in Content between Canadians and Italians

2012· article· en· W2132964674 on OpenAlexaffabout
Marco Zanasi, Teresa L. DeCicco, Allyson Dale, G. Musolino, Caroline Wright

Bibliographic record

VenueImagination Cognition and Personality · 2012
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsTrent University
Fundersnot available
KeywordsDreamPsychologyContent (measure theory)Content analysisSexual behaviorDevelopmental psychologyGender studiesSocial psychologyDemographySociologyAnthropology

Abstract

fetched live from OpenAlex

This research extends previous investigations on dreams with sexual imagery while beginning the empirical investigation across cultures and between genders. To extend previous cultural research, sexual dream imagery and frequency between Italian and Canadian men and women were examined. The first study consisted of two samples of 267 dreams (112 male and 155 female) from Trent University, Canada and Tor Vergata University, Italy. The second study consisted of two samples of 100 dreams with sexual content (50 male and 50 female) from Canadian and Italian students. Computer textual analysis of dream content categories revealed that sexual imagery and frequency of sexual dream content was consistent with previous research. New findings were found between the two cultures and across gender. For dreams in general, differences were found between Italian males and Italian females, and, between Italian females and Canadian females. These studies show that both cultural and gender differences are relevant for dreams with sexual imagery. Further investigations are warranted and should be extended to other cultural groups now that the protocol has been established through these studies. Limitations and suggestions for future research are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.172
GPT teacher head0.327
Teacher spread0.155 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2012
Admission routes2
Has abstractyes

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