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Record W4232411656 · doi:10.32920/ryerson.14657175.v1

"Why nobody told me and why it would have been impossible to do so until now" : an autoethnographic inquiry into teaching and learning towards social justice in early childhood teacher education

2021· preprint· en· W4232411656 on OpenAlexaff
Katrina Ramnarase

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood Development
FundersTehran University of Medical Sciences and Health Services
KeywordsAutoethnographyTransformative learningPedagogyNarrative inquiryNarrativeExperiential learningEarly childhood educationEarly childhoodSociologyPsychologyTeacher educationSocial science

Abstract

fetched live from OpenAlex

In this paper, a personal narrative autoethnographic methodology is used to begin mapping a transformative learning journey towards teaching and learning for social justice in early childhood teacher education. In autoethnography, personal lived experience is the primary source of data. This inquiry explores two stories of personal transformative learning using a journey metaphor to structurally frame the inquiry. Through a process of writing as inquiry (Richardson, 2003) and emotional introspection (Ellis, 1991) and using a conceptual framework based on postmodern perspectives, this autoethnographic research paper reveals the steps toward critical consciousness (Freire, 2006) taken by the author/researcher-a student in early childhood teacher education-as she uses personal narratives of lived experience in early childhood teacher education as primary data to explore the implications of this transormative learning process to explore themes around teaching and learning towards social justice in early childhood teacher education programs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.003
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 head0.039
GPT teacher head0.387
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 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
Published2021
Admission routes1
Has abstractyes

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