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Record W2004263100 · doi:10.1080/1554480x.2014.951652

The palimpsest layers of pre-service teachers’ literacy autobiographies

2014· article· en· W2004263100 on OpenAlexaff
Wendy D. Bokhorst‐Heng, Joan B. Flagg-Williams, Stewart West

Bibliographic record

VenuePedagogies An International Journal · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsCrandall University
Fundersnot available
KeywordsLiteracyCritical literacyNarrativePedagogyMeaning (existential)Context (archaeology)SociologyMathematics educationPsychologyHistoryLiteratureArt

Abstract

fetched live from OpenAlex

In this article, we examine three literacy autobiographies written by pre-service teachers. Narratives are seen as not just stories relating a set of facts, but rather a means by which individuals interpret their experience. Literacy autobiographies are a reflective and interpretive account of one’s development as a literate being. Using the tools of narrative analysis, we (a) examine these stories to understand the processes of literacy development through the experiences of learners’ storied lives; (b) seek to understand the impact that teachers’ literacy journeys have on their view of literacy and literacy education; and (c) explore what these literacy autobiographies reveal about the contributions of teacher reflection to pre-service teacher education. Our analysis points to the importance of personal relationships in the development of literacy, providing the context within which literacy practices give meaning to the literacy events active in the narrators’ lives. We also note a persistent view of traditional forms of literacy in contrast to pre-service teachers’ involvement in multiliteracies, and argue that this gap needs to be addressed in order to prepare teachers for the twenty-first century classroom. We also consider how reflection can be a more intentional aspect of pre-service teacher education to enhance pedagogy and learning.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.328
Teacher spread0.291 · 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.

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

Citations6
Published2014
Admission routes1
Has abstractyes

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