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Record W3098963855 · doi:10.1177/0973184920969338

Feminist Dialogic Pedagogical Spaces in Teacher Education: Practical Inclusivity, Eye-opening and Change

2020· article· en· W3098963855 on OpenAlexaffabout
Olga Shugurova

Bibliographic record

VenueContemporary Education Dialogue · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDialogicPedagogyFeminist pedagogySociologyScholarshipSituatedCritical consciousnessCritical pedagogyCritical theoryContext (archaeology)Sociocultural evolutionFeminist philosophyFeminismGender studiesPolitical science

Abstract

fetched live from OpenAlex

In this reflective article, I explore a feminist dialogic pedagogy of inclusive education (IE) in the sociocultural context of my and my students’ lived experience. I ask what a feminist dialogic pedagogy means to my students. The purpose of this article therefore is to advance knowledge about a feminist dialogic pedagogy in teacher education with a focus on the formation of students’ critical consciousness of IE philosophy that is currently mandated by all Canadian provinces. My intention is to contribute to an evolving scholarship of feminist pedagogy in teacher education programmes with a grounded and creative understanding of teacher candidates’ lived experience. The article argues that feminist dialogic pedagogy creates a space of inclusion for all students. Despite and across social differences, this pedagogy leads a conscious change among students, impacting their daily lives. Consequently, students successfully achieve their academic goals because they feel critically attentive to and conscious of their situated knowledge ( Haraway, 1988 , Feminist Studies, vol. 14, pp. 575–599) in educational institutions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.014
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.053
Scholarly communication0.0120.013
Open science0.0020.016
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.221
GPT teacher head0.440
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes2
Has abstractyes

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