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Record W3003266943 · doi:10.1177/1476718x19898715

Creating identity texts with young children across culturally and linguistically diverse contexts

2020· article· en· W3003266943 on OpenAlexaffabout
Jodi Streelasky

Bibliographic record

VenueJournal of Early Childhood Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIdentity (music)Perspective (graphical)PsychologyPedagogySociologyConventionVisual artsSocial scienceAesthetics

Abstract

fetched live from OpenAlex

This article addresses the ways young children in culturally and linguistically diverse settings were involved in the meaningful development of identity texts in the form of personalized books. In the study, Canadian and Tanzanian children aged 4 to 6 shared their favorite learning experiences and spaces at school through their use of multiple modes. A multimodal approach to data sharing was then implemented through the co-creation of three dual-language books in English and Kiswahili. The books featured the research participants’ images, drawings, paintings, and photographs, and included verbal descriptions of their multimodal texts in their distinct geographical and cultural contexts. The children in both settings were involved in the book-making process by sharing their views on what images and descriptions they wanted to include in the identity texts that were then shared with both groups of children, their teachers, and their families. This approach to research and data dissemination with children draws on the United Nations Convention on the Rights of the Child which views children as strong, capable, and knowledgeable. This perspective also recognizes the rights of children to participate in decision-making processes in research in which they are involved, and to be empowered to communicate their own views.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.056
GPT teacher head0.399
Teacher spread0.343 · 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 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

Citations9
Published2020
Admission routes2
Has abstractyes

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