MétaCan
Menu
Back to cohort
Record W2164335645 · doi:10.1111/1467-8624.00374

Problem Solving, Contention, and Struggle: How Siblings Resolve a Conflict of Interests

2001· article· en· W2164335645 on OpenAlexafffund

Bibliographic record

VenueChild Development · 2001
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Waterloo
FundersGovernment of Ontario
KeywordsNegotiationConstructivePsychologyConflict resolutionSiblingSocial psychologySibling relationshipQuality (philosophy)Developmental psychologyProcess (computing)Political scienceEpistemologyLawComputer science

Abstract

fetched live from OpenAlex

In a laboratory setting, 48 sibling dyads age 4 and 6 or 6 and 8 years negotiated the division of six toys. Findings revealed that, in general, children reached divisions while using a preponderance of constructive problem-solving strategies, rather than contentious tactics. The degree of conflict of interests and the quality of sibling relationships predicted the children's use of problem-solving and contentious negotiation strategies, and was related to the types of resolutions achieved. Dyads experiencing low conflict of interests resolved their differences quickly. High conflict of interests coupled with positive relationships and constructive negotiation resulted in longer negotiations and creative, agreeable resolutions. High conflict of interests coupled with more negative relationships and destructive negotiations resulted in children's failures to reach agreement. Developmental differences indicated that older siblings within the pairs took the lead in negotiation, and benefited slightly more from the divisions. Furthermore, children in older dyads were more sophisticated and other oriented in their negotiations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.922

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)0.0010.000

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 head0.036
GPT teacher head0.284
Teacher spread0.248 · 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

Citations38
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueChild DevelopmentSame topicChild and Animal Learning DevelopmentFrench-language works237,207