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Record W2124547484 · doi:10.13185/1444

Fostering Conceptual Roles for Change: Identity and Agency in ESEA Teacher Preparation

2010· article· en· W2124547484 on OpenAlexaff
Brian Morgan

Bibliographic record

VenueKritika Kultura · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsYork University
Fundersnot available
KeywordsPedagogyBlueprintTransformative learningContext (archaeology)Agency (philosophy)CurriculumTeacher educationIdentity (music)SociologyPsychology

Abstract

fetched live from OpenAlex

English Changing, the theme and title of the 2009 ESEA conference held in Manila, raises specific challenges for language teacher education: To what extent do we prepare teachers to be passive recipients of the social, cultural, and economic changes that align with the global spread of English? Alternatively, how might we encourage teachers to become active participants—“agents of change”—through their mediation and implementation of language curricula and pedagogy? The author addresses such questions by first reflecting on his own personal and professional development in EFL and EAP teaching contexts. These experiences are then related to the growing research literature in language teacher identity and several theoretical issues related to this area of interest. The following sections of the article look at the complexities of transferring theory to practice in the specific context of a pre-service, language teacher education course, one of whose primary goals is to foster awareness of language as a social practice linked to unequal relations of power, and one in which language teachers are encouraged to imagine and act otherwise through their teaching and interpersonal relationships with students and colleagues. In the final sections, these course aspirations are explored through a group assignment called a “social issues project,” in which students conceptualize and design a blueprint for transformative action in various forms such as an advocacy letter, workshop, curricular materials, etc. Reflection on the strengths and weaknesses of several selected projects, and how they relate with ESEA issues, conclude the article.

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)0.014
metaresearch head score (Gemma)0.012
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 score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0180.064
Scholarly communication0.0180.013
Open science0.0020.015
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.333
Teacher spread0.238 · 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

Citations48
Published2010
Admission routes1
Has abstractyes

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