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Record W2133280012 · doi:10.37119/ojs2013.v19i1.41

Becoming Pedagogical: Sustaining Hearts With Living Credos

2013· article· en· W2133280012 on OpenAlexafffundvenue
Carl Leggo, Rita L. Irwin

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBachelorCreativityCurriculumPedagogyClass (philosophy)ToolboxSociologyThe artsMathematics educationTeacher educationPsychologyVisual artsArtPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

From September to December 2009, a class of teacher candidates completed a Bachelor of Education course titled English Language Arts: Secondary Curriculum and Instruction. The instructor introduced himself at the beginning of the course as an a/r/tographer who is an artist, a researcher, and a teacher. He invited students to think about the possibilities of their being a/r/tographers, and to think about how they live in the world, as well as in their new emerging identities in the Bachelor of Education program, as artists and researchers and teachers. The teacher candidates were invited to think about how they werebecoming pedagogical, and how they could sustain their hearts in the dynamic and complex process of becoming pedagogical.They were reminded that teacher candidates are not learning a toolbox of skills and strategies for teaching; they are learning how to navigate the tangled and complex world of human beings in communities called schools.Keywords: teacher education; lifewriting; a/r/tography; credo; creativity

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)0.003
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.016
Scholarly communication0.0140.008
Open science0.0010.017
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.066
GPT teacher head0.310
Teacher spread0.244 · 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

Citations11
Published2013
Admission routes3
Has abstractyes

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