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Record W3208171178 · doi:10.1111/cdev.13687

Children strategically conceal selfishness

2021· article· en· W3208171178 on OpenAlexfundno aff
Gorana Gonzalez, Richard E. Ahl, Sara Cordes, Katherine McAuliffe

Bibliographic record

VenueChild Development · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchJohn Templeton Foundation
KeywordsSelfishnessPsychologyUltimatum gameEndowmentExploitSocial psychologyMicroeconomicsEconomicsPolitical scienceComputer securityLawComputer science

Abstract

fetched live from OpenAlex

Can children exploit knowledge asymmetries to get away with selfishness? This question was addressed by testing 6- to 9-year-old children (N = 164; 81 girls) from the Northeastern United States in a modified Ultimatum Game. Children were assigned to the roles of proposers (who offered some proportion of an endowment) and responders (who could accept or reject offers). Both players in the Informed condition knew the endowment quantity in each trial. However, in the Uninformed condition, only proposers knew this information. In this condition, many proposers made "strategically selfish" offers that seemed fair based on the responders' incomplete knowledge but were actually highly selfish. These results indicate that even young children possess the ability to deceive others about their selfishness.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

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)0.0010.000

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 head0.020
GPT teacher head0.310
Teacher spread0.289 · 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.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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