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Record W2159183591 · doi:10.1037/lhb0000061

Young children’s understanding that promising guarantees performance: The effects of age and maltreatment.

2013· article· en· W2159183591 on OpenAlexaff
Thomas D. Lyon, Angela D. Evans

Bibliographic record

VenueLaw and Human Behavior · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsBrock University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyChild abuseDevelopmental psychologyChild neglectCharacter (mathematics)Poison controlHuman factors and ergonomicsSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Two studies, with 102 nonmaltreated 3- to 6-year-old children and 96 maltreated 4- to 7-year-old children, examined children's understanding of the relative strengths of "I promise," "I will," "I might," and "I won't," to determine the most age-appropriate means of eliciting a promise to tell the truth from child witnesses. Children played a game in which they chose which of 2 boxes would contain a toy after hearing story characters make conflicting statements about their intent to place a toy in each box (e.g., one character said "I will put a toy in my box" and the other character said "I might put a toy in my box"). Children understood "will" at a younger age than "promise." Nonmaltreated children understood that "will" is stronger than "might" by 3 years of age and that "promise" is stronger than "might" by 4 years of age. The youngest nonmaltreated children preferred "will" to "promise," whereas the oldest nonmaltreated children preferred "promise" to "will." Maltreated children exhibited a similar pattern of performance, but with delayed understanding that could be attributed to delays in vocabulary. The results support a modified oath for children: "Do you promise that you will tell the truth?".

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 score0.081
Threshold uncertainty score0.326

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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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