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Record W4413342868 · doi:10.1080/15248372.2025.2547629

Familiar Magic Helps Children See That Fantastical Events Can Happen in Stories

2025· article· en· W4413342868 on OpenAlexafffundabout
Emily Stonehouse, Terryn Kim, Ori Friedman

Bibliographic record

VenueJournal of Cognition and Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMAGIC (telescope)PsychologyStory tellingNarrativePsychoanalysisSocial psychologyLiteratureArt

Abstract

fetched live from OpenAlex

Young children often show a reality bias when thinking about fiction—they say that stories can include realistic events, while rejecting fantastical events. We sought to better understand this bias by looking at circumstances in which it is reduced. In two experiments, children ages 3–7 (total N = 404) in Canada judged whether ordinary story protagonists could complete goals using magical items (e.g. a magic wand), as well as regular items useful or irrelevant for the goals. In both experiments, children at all ages mostly agreed that the characters could use the magical objects. Children, then, may overcome the reality bias when familiar fantasy elements help them envisage how fantastical events could happen in a story. At the same time, children still showed some signs of realism. They more strongly agreed that story characters could use the useful real-world than magical objects to complete the goals. Also, when realistic and fantastic ways of completing goals were pit against each other, children overwhelmingly said characters used the realistic method. We also found that the reality bias was not specifically affected by whether fantasy elements were introduced directly in the story or only later when children were asked about what could happen in the story. Overall, our findings suggest that although children are not strictly restricted to anticipating realism in stories, they strongly prefer it when it is directly compared with fantasy.

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 score0.183
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 head0.022
GPT teacher head0.295
Teacher spread0.272 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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