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Record W4390051570 · doi:10.3390/socsci13010009

Child-Led Research with Young Children: Challenging the Ways to Do Research

2023· article· en· W4390051570 on OpenAlexfundno aff
E. Kay M. Tisdall, Emma Clarkson, Lynn McNair

Bibliographic record

VenueSocial Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
FundersEconomic and Social Research CouncilSocial Sciences and Humanities Research Council of CanadaFroebel Trust
KeywordsDocumentationPsychologyEducational researchPractitioner researchDevelopmental psychologyMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

Child-led research is gaining increasing attention. Such research involves children leading throughout the research process, from research design to dissemination. Child-led research has tested adult-centric research assumptions, with debates in the literature about researchers’ expertise and responsibilities. If these debates are testing for child-led research undertaken with older children and young people, they are even more so for young children below school-starting age. This article examines child-led research undertaken in a Froebelian early years setting, over 11 months, with 36 children aged between 2 and 5 years, from the adult facilitators’ perspectives. The article utilises the research’s documentation, including mind maps, photographs and story books, songs and video recordings, and an interview undertaken with the facilitating early years practitioner and supporting academic. Learning from this, the article challenges the assumption, in much of the literature on child-led research, that adults need to transmit their knowledge of research methods to children. Instead, a ‘slow pedagogy’ can build on children’s own knowledge, collectively, with time to come to research understandings. The article concludes that child-led research is feasible with young children, but the research process can include or exclude certain forms of children’s communication, making some children more ‘competent’ to undertake research than others.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.008
Science and technology studies0.0210.003
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.298
GPT teacher head0.490
Teacher spread0.191 · 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 designTheoretical or conceptual
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

Citations9
Published2023
Admission routes1
Has abstractyes

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