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Record W3173184640 · doi:10.1159/000517221

The Development of Giving in Forms of Object Exchange: Exploring the Roots of Communication and Morality in Early Interaction around Objects

2021· article· en· W3173184640 on OpenAlexaff
Jeremy I. M. Carpendale, Ulrich Müller, Beau Wallbridge, Tanya Broesch, Thea Cameron‐Faulkner, Kayla Ten Eycke

Bibliographic record

VenueHuman Development · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsSocial relationObject (grammar)PsychologyMoralityProcess (computing)NaturalismSocial psychologySocial exchange theoryEpistemologySociologyCognitive scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Giving is an act of great social importance across cultures, with communicative as well as moral dimensions because it is linked to sharing and fairness. We critically evaluate various explanations for how this social process develops in infancy and take a process-relational approach, using naturalistic observations to illustrate forms of interaction involving the exchange of objects and possible developmental trajectories for the emergence of different forms of giving. Based on our data, we propose that the object becomes a pivot point for interaction, and through the process of such interaction the social actions of showing and giving emerge and take on diverse social meanings within the relations between infants and caregivers.

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)0.004
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.014
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.002
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 head0.082
GPT teacher head0.330
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 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

Citations15
Published2021
Admission routes1
Has abstractyes

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