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Record W3005883455 · doi:10.3389/fpsyg.2020.00177

An Old Mechanism, Imitation, Geared for Socio-Material Knowing in a “Day in the Life” of First Graders

2020· article· en· W3005883455 on OpenAlexaff
Giuliana Pinto, Catherine Ann Cameron, Monica Toselli

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImitationPerspective (graphical)Flexibility (engineering)PsychologyEveryday lifeFrame (networking)Mechanism (biology)Through-the-lens meteringCognitive psychologySocial psychologyEpistemologyComputer scienceArtificial intelligenceLens (geology)

Abstract

fetched live from OpenAlex

This paper adopts sociomateriality as a theoretical lens to further our understanding of how imitation acts to support the use of objects, and in doing so, constitues a sociomaterial practice. Within a sociomaterial perspective we aimed to perform the analysis of imitation as a powerful way to learn how to use objects embedded into the practices within which the objects are constituted. The contribution of this approach is illustrated using the findings of the application of the quasi-ecological DITL methodology to the everyday lives of two 6-year-old children. Within a case-study frame, we traced the children’s imitation behaviors focused on the use of objects during an entire day of their life, the various people and practices with which they were associated, the multiple sociomaterial configurations that the objects assume, and the social and material consequences of their use. Imitation appears to be is a complex activity, involving multiple stakeholders who interact in order to facilitate the understanding of various artifacts across diverse knowledge domains, and enhance their interpretive flexibility across communities of practice.

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)0.001
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 score0.452
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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