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Record W2076700638 · doi:10.1089/cyber.2013.0494

Comparing the Effects of Real Versus Simulated Violence on Dream Imagery

2014· article· en· W2076700638 on OpenAlexaffabout
Allyson Dale, Anthony Murkar, Nicolle Miller, J. E. Black

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2014
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsTrent UniversityUniversity of Ottawa
Fundersnot available
KeywordsDreamPsychologyArtificial intelligenceCartographyComputer scienceGeographyNeuroscience

Abstract

fetched live from OpenAlex

Participants in the current study were 75 males, including 25 Canadian soldiers, 25 heavy gamers who play military based video games such as "Call of Duty," and a control group comprised of 25 males. One dream per participant was analyzed using Hall and Van de Castle content analysis guidelines, including aggression, threat, and previously established scales for intensity of aggression and emotion. The dreams of soldiers had a higher frequency of both aggression and threat, and were also more intense in aggression and emotion than both the heavy gamers and the controls. These findings suggest that exposure to real life violence and threat (as well as the emotional significance of the experience) is more frequently incorporated into dream imagery than simulated or virtual threat. Limitations and directions for future studies 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 score0.439
Threshold uncertainty score0.631

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 studies0.0010.000
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 head0.029
GPT teacher head0.328
Teacher spread0.299 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

Explore more

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