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Record W4312186001 · doi:10.37119/ojs2022.v28i1b.661

Counter-Storytelling: A Form of Resistance and Tool to Reimagine More Inclusive Early Childhood Education Spaces

2022· article· en· W4312186001 on OpenAlexaffvenueabout
Kamogelo Amanda Matebekwane

Bibliographic record

Venuein education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsStorytellingResistance (ecology)NarrativeIdeologySociologyCritical race theoryGender studiesAestheticsRace (biology)PoliticsPolitical scienceArtLiteratureLaw

Abstract

fetched live from OpenAlex

In this essay, I reflect on my lived experiences as a girl child growing up in my home country of Botswana, and also as a mother in a foreign country, Canada. I am experimenting with my personal essay and making connections with academic articles that will help me understand my behaviors, attitudes, and responses to challenging situations that seemed unfair and unjust. I believe sharing my experiences not only gives me a platform to reflect, but also renders an opportunity to unearth hidden ideologies that perpetuate dominant discourses that continue to undesirably affect early childhood education. Sharing the unfortunate events for me brings healing and comfort. My essay is guided by critical race theory that provokes and challenges the normalized practices in education that continue to marginalize the minority community. Also, my inspiration for this piece was drawn from Wallace and Lewis’s (2020) book, which described humans as narrative creatures who need stories/narratives to make sense of the world around them. The essay unpacks and discusses four critical questions, at the same time, offering acts of resistance and refusal by applying counter-storytelling methodology. Keywords: counter-storytelling, critical race theory, lived experiences, racialized minorities, early childhood education, acts of resistance and refusal

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.291
Teacher spread0.284 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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