MétaCan
Menu
Back to cohort
Record W2901471848 · doi:10.5539/jedp.v9n1p1

Do Children Recognize That Kinship Relationships Have an Innate Biological Basis?

2018· article· en· W2901471848 on OpenAlexvenueno aff
Lakshmi Raman

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
Fundersnot available
KeywordsKinshipSiblingCreaturesPsychologyDevelopmental psychologySociologyGeographyNatural (archaeology)Anthropology

Abstract

fetched live from OpenAlex

Three studies were conducted to investigate if four and five year old children recognize that kinship relationships are determined by biological associations and not environmental conditions. All three studies employed the “switched-at-birth” task. Study 1 investigated if children and adults recognize who the biological parents and siblings are. Study 2 examined preschoolers’ and adults’ recognition of who the biological parents and siblings are when step parents and step siblings were introduced into the family. Study 3 examined if children and adults extend their knowledge of kinship relationships to non-human creatures. For Studies 1 and 2, results indicated that preschoolers and adults have a robust and accurate biological model of kinship for both biological parents and sibling relationships. However in Study 3, preschoolers had a more difficult time recognizing biological sibling relationships than biological parent relationships in the presence of step parents and step siblings for non-human biological creatures. In totality, these results suggest that even young children (like adults) have a robust theory of kinship when reasoning about human relationships. However children’s model of kinship is fragile and still developing when reasoning and extending their knowledge about humans to non-human species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.129
GPT teacher head0.375
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational and Developmental PsychologySame topicChild and Animal Learning DevelopmentFrench-language works237,207