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Record W2137975547 · doi:10.37119/ojs2010.v16i2.99

A Continuum of Learning: Enhancing Connections Between Teacher-Candidates and Education Graduate Students Through a Narrative Framework

2013· article· en· W2137975547 on OpenAlexaffvenue
Lisa Mitchell

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsMentorshipNarrativeNarrative inquiryHonourTeacher educationExperiential learningPedagogyCurriculumIdentity (music)PsychologyMathematics educationSociologyMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

This paper was written to complement the book review; "What’s Your Story? A Book Review of Leah Fowler’s A Curriculum of Difficulty: Narrative Research in Education and the Practice of Teaching" (2006), which can also be found in this issue of in education. This paper challenges teacher-education professionals to consider the benefits of creating and facilitating meaningful mentorship opportunities between teacher-candidates and education graduate students. This paper discusses Fowler’s (2006) model for narrative inquiry and its relationship to the formation of teacher identity and explores whether or not this particular model can support the creation of sustainable and effective mentoring relationships in current teacher-education programs. Teacher-candidates and graduate students alike will both come to a “deeper understanding of the relationship among past, present, and projected senses of self” (Sumara & Luce-Kapler, 1996) as they engage in mutually beneficial, critically reflective learning practices. Purposeful construction of mentorship opportunities that honour the experiential stories of individuals may serve to further increase education students’ awareness of their dynamic position along a continuum of learning in both undergraduate and graduate contexts.Keywords: narrative research; mentorship; teacher identity

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.439
Teacher spread0.399 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2013
Admission routes2
Has abstractyes

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