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Record W2140462348 · doi:10.1177/0956797614536401

Can Classic Moral Stories Promote Honesty in Children?

2014· article· en· W2140462348 on OpenAlexaff
Kang Lee, Victoria Talwar, Anjanie McCarthy, Ilana Ross, Angela D. Evans, Cindy Arruda

Bibliographic record

VenuePsychological Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsBrock UniversityMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsHonestyDishonestyLyingPsychologyVirtueGeorge (robot)Social psychologyDeceptionMoral developmentFocus (optics)Developmental psychologyEpistemologyPhilosophyHistoryMedicine

Abstract

fetched live from OpenAlex

The classic moral stories have been used extensively to teach children about the consequences of lying and the virtue of honesty. Despite their widespread use, there is no evidence whether these stories actually promote honesty in children. This study compared the effectiveness of four classic moral stories in promoting honesty in 3- to 7-year-olds. Surprisingly, the stories of "Pinocchio" and "The Boy Who Cried Wolf" failed to reduce lying in children. In contrast, the apocryphal story of "George Washington and the Cherry Tree" significantly increased truth telling. Further results suggest that the reason for the difference in honesty-promoting effectiveness between the "George Washington" story and the other stories was that the former emphasizes the positive consequences of honesty, whereas the latter focus on the negative consequences of dishonesty. When the "George Washington" story was altered to focus on the negative consequences of dishonesty, it too failed to promote honesty in children.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.101
GPT teacher head0.330
Teacher spread0.228 · 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

Citations158
Published2014
Admission routes1
Has abstractyes

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