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Record W4403605861 · doi:10.1080/09518398.2024.2416700

A posthumanist critical multilogue: storytelling as bicultural teaching and learning in early childhood education in Aotearoa New Zealand

2024· article· en· W4403605861 on OpenAlexaff
Alison Warren

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsAotearoaStorytellingPedagogyEarly childhood educationSociologyCritical theoryBiculturalismPsychologyGender studiesNarrativeEpistemologyArt

Abstract

fetched live from OpenAlex

A posthumanist critical multilogue may be understood as a many-voiced conversation where the concept of voice encompasses multiple ways of expressing in networks of enmeshed relations among humans and non-humans. A multilogue is critical when power relations are mapped, and posthumanist when contributions to multilogue conversations emerge from relations among human and non-human agencies. During research into how bicultural teaching and learning is lived in one early childhood education setting, teachers shared accounts of children engaging playfully with the Māori pūrākau/story of Hatupatu and Kurangaituku (Birdwoman). A posthumanist critical multilogue explores what might be produced in critical, curious, and creative entanglements of Māori and posthumanist concepts and theories, policies and practices of bicultural teaching and learning, and human and non-human bodies. Through transversal processes wandering from children to teachers, to communities, landscapes, and histories, creative possibilities are glimpsed.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.489
Teacher spread0.432 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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