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Record W2466339419 · doi:10.1177/0022022116655788

Dreams of Canadian Students

2016· article· en· W2466339419 on OpenAlexaffabout
Allyson Dale, Monique Lortie‐Lussier, Christina G. Wong, Joseph De Koninck

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDreamNormativeContent (measure theory)PsychologyContent analysisSocial psychologySample (material)Developmental psychologySociologySocial scienceEpistemologyMathematics

Abstract

fetched live from OpenAlex

A total of 600 dream reports were collected from 300 Canadian university students, 150 female and 150 male, and their content analyzed with the Hall and Van de Castle (1966) system of categories. The main dream content categories were characters, aggressive and friendly interactions, positive and negative emotions, and dream outcomes. The main purpose of the analysis was to provide normative data for a large sample of young Canadians to determine (a) whether negative elements prevail over positive ones, as assumed by the threat simulation theory and (b) whether dream gender differences are consistent with differences in waking life, in accordance with the continuity hypothesis. Overall, findings support both theories. The final objective was to compare the Canadian data, relative to gender differences, with normative data established in 1966 with the original American sample. Findings for males and females and gender differences remain consistent with the American normative data for most categories despite a 50 years interval. Similarities in Canadian and American dream content reflect similarities between the respective cultures. They also attest to fundamental structural dimensions of dream content that transcend cultures. Other types of content analysis relative to themes, for instance, might be appropriate to highlight cultural differences.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.474
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.110
GPT teacher head0.474
Teacher spread0.364 · 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 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

Citations12
Published2016
Admission routes2
Has abstractyes

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