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Record W3043210780 · doi:10.20360/langandlit29457

Narrative Inquiry as Relational Research Methodology and Andragogy: Adult Literacy, Identities and Identity Shifting

2020· article· en· W3043210780 on OpenAlexaffvenue
Sandra Jack-Malik, Janet L. Kuhnke

Bibliographic record

VenueLanguage and Literacy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsCape Breton University
Fundersnot available
KeywordsAndragogyNarrativePedagogyLiteracyTUTORIdentity (music)CurriculumContext (archaeology)Narrative inquirySociologyPsychologyAdult educationAestheticsLinguistics

Abstract

fetched live from OpenAlex

Using narrative inquiry as a relational methodology and as andragogy, the research puzzle was to deepen understanding of the experiences of women, living with limited literacies and as they engaged in tutoring. This work animates the temporal, curriculum and life making experiences of a tutee and tutor within the context of adult literacy with a focus on learning to write. As the study progressed and as trust developed, tension filled stories were experienced, shared and reimagined. Thinking through the lens of Dewey’s continuity of experience we demonstrate the links between literacies, curriculum making, and efforts to shift identities. Field texts provided textured and nuanced descriptions of narrative inquiry as andragogy, while supporting the tutee to expand her literate identity and the tutor to become more relational. This work invites readers to reimagine the ways in which educators practice alongside adults who are described as struggling readers and writers.

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)0.033
metaresearch head score (Gemma)0.029
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: Methods · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0060.050
Scholarly communication0.0170.015
Open science0.0020.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.172
GPT teacher head0.545
Teacher spread0.373 · 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
GenreMethods

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

Citations8
Published2020
Admission routes2
Has abstractyes

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