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Record W4409737965 · doi:10.1016/j.lecon.2025.100001

Teacher educator professional learning in context: Findings from the Reading Pedagogies of Equity Project

2024· article· en· W4409737965 on OpenAlexfundno aff
Lori McKee, Rachel Heydon, Sandra Poczobut, Pamela J. McKenzie, Zheng Zhang

Bibliographic record

VenueLearning in Context · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaWestern University
KeywordsEquity (law)Reading (process)Context (archaeology)PedagogySociologyProfessional developmentPsychologyMathematics educationPolitical scienceHistory

Abstract

fetched live from OpenAlex

Teacher educators are vital for promoting teacher education for school equity, and their own professional learning is necessary for supporting this endeavour. However, research on equity-focused professional learning for teacher educators (PLTE) that accounts for their contexts is limited. Oriented through critical posthumanities and drawing on data from 24 teacher educator participants from the Reading Pedagogies of Equity Project, a PLTE focused on critical reading praxes, this study considered the sociomaterial contexts of the participants, the needs and desires these produced for them, and the implications for critical, equity-focused PLTE. The study identified teacher educators as a diverse group, including through their positioning in the workplace, roles and responsibilities, and access to collaboration with other teacher educators. This diversity generated a demand for situated professional learning with delivery modes geared to access and engagement, flexible structures, and content that was meaningful to all.

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)0.027
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 score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0040.004
Open science0.0010.012
Research integrity0.0020.003
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 head0.191
GPT teacher head0.469
Teacher spread0.278 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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