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Record W3092698533 · doi:10.3390/educsci10100288

Literacy Teacher Educators Creating Space for Children’s Literature

2020· article· en· W3092698533 on OpenAlexafffund
Lydia Menna, Clare Kosnik, Pooja Dharamshi

Bibliographic record

VenueEducation Sciences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of TorontoSimon Fraser UniversityUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLiteracyVariety (cybernetics)PsychologyPedagogyMathematics educationCritical literacySpace (punctuation)Qualitative researchSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper reports on a qualitative research study that examined how 10 literacy teacher educators (LTEs) utilized children’s literature to invite teacher trainees to critically engage with social issues, challenge their assumptions about literacy, and begin to develop the knowledge and dispositions to work alongside diverse learners (e.g., culturally, linguistically, socio-economically). The LTEs recognized that teacher trainees often entered their literacy courses with restricted conceptions of literacy and deficit assumptions about children from economically marginalized and/or culturally and linguistically diverse backgrounds. Within their courses, the LTEs positioned literacy as a multifaceted social practice, wherein access to a variety of representational resources facilitates the active construction of knowledge and identities. The LTEs modeled instructional strategies and designed assignments that encouraged teacher trainees to use children’s literature as a means to connect with issues relevant to the lives of young learners within contemporary classrooms. This research will be of interest to LTEs who endeavor to use children’s literature as a springboard to support teacher trainees to develop a self-reflective stance and a critical cultural consciousness.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.084
GPT teacher head0.432
Teacher spread0.348 · 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 designNot applicable
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

Citations6
Published2020
Admission routes2
Has abstractyes

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