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Record W4312219105 · doi:10.1163/15718182-30040005

Dolls as a Rights-Affirming Early Childhood Research Method

2022· article· en· W4312219105 on OpenAlexaff
Donna Koller, Ellie Murphy

Bibliographic record

VenueThe International Journal of Children s Rights · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmpirical researchEarly childhoodPsychologyDevelopmental psychologyEpistemology

Abstract

fetched live from OpenAlex

Abstract In honouring children’s rights to expression and participation in the world around them, we asked “How do researchers use dolls to elicit young children’s perspectives?” We conducted a scoping review of the literature on doll use in early childhood research published between 2004 and 2021. Following an exhaustive search using established methods for evaluating empirical research, 28 studies were included in this review. Patterns, advances and gaps in the literature indicated the benefits of doll use, as well as guidance and future directions for researchers. Dolls offer a practical avenue for exploring perspectives across a range of topics and settings, giving voice to diverse young children whose perspectives are often overlooked.

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)0.087
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.010
Science and technology studies0.0040.007
Scholarly communication0.0050.008
Open science0.0030.010
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.046
GPT teacher head0.412
Teacher spread0.366 · 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 designQualitative
Domainnot available
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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