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Record W608105767 · doi:10.5206/cie-eci.v37i2.9120

Learning to Teach, Learning to Play the Game: A Case Study of a Minority Teacher Candidate Teaching for Equity, Diversity and Social Justice

2008· article· en· W608105767 on OpenAlexaffvenue
Andrew M. Allen

Bibliographic record

VenueComparative and International Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPracticumPedagogyEquity (law)SociologySocial justicePremiseTeacher educationDiversity (politics)Political scienceSocial scienceLawEpistemology

Abstract

fetched live from OpenAlex

In this article, the author explores the premise that possibilities for social change in an antiracist teacher education program might be revealed by closely examining the struggles and real life experiences of a beginning teacher from a minority background working from the margins of the educational setting. Focusing on the quality of the teaching-learning activities being negotiated and forged, this article is grounded in a study of the tensions, dilemmas and conflicts experienced by teacher candidates learning to teach for social justice. The study revealed that the inherent tensions caused by discontinuities between the intent and critical antiracist philosophy of the program and its actual content, processes and the experience within practicum schools can be necessary for moving teacher candidates in their thinking about antiracist teaching and teaching critically. The article’s focus is on one participant from a Latino background who began to come to terms with accepting the existence of tensions and conflicts and became less obsessed with finding definite solutions or resolutions. Explicit discussion of his experiences within all aspects of the program and direct talk about the tensions and conflicts began to help the teacher candidate make sense of his experiences. Critical teaching for equity, diversity and social justice is a journey into teaching filled with risks and uneasiness. Dans cet article, l'auteur explore la prémisse que l'on peut révéler les possibilités du changement social dans un programme d'éducation antiraciste pour les enseignants stagiaires en examinant de près les luttes et les expériences de vie d'un enseignant stagiaire débutant de souche minoritaire et qui travaille en marge du milieu d'éducation. Se concentrant sur la qualité des activités d'enseigner et d'apprendre en négociation et en formation, l'article est fondé sur une étude des tensions, des dilemmes et des conflits subis par les enseignants stagiaires quand ils apprennent à enseigner la justice sociale. L'étude révèle que les tensions inhérentes causées par les discontinuités entre l'intention et la philosophie antiraciste du programme et son contenu, les processus et l'expérience dans les écoles peuvent être nécessaires pour changer la façon de penser des enseignants stagiaires sur l'enseignement antiraciste et l'enseignement critique. L'article est centré sur un participant de souche Latino-américaine qui commence à accepter l'existence des tensions et des conflits et à devenir moins obsédé par la découverte des solutions ou des résolutions définitives. Une discussion explicite de ses expériences dans tous les aspects du programme et un entretien direct sur les tensions et conflits commencent à aider cet enseignant stagiaire à donner un sens à ses expériences. Enseignement critiquement de l'égalité, la diversité et la justice sociale représente un voyage dans l'enseignement et est rempli de risques et d'inquiétudes et non une destination.

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)0.004
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0280.010
Scholarly communication0.0070.004
Open science0.0050.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.159
GPT teacher head0.498
Teacher spread0.338 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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