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Record W2755995460 · doi:10.1177/1077800417727762

The Centrality of Reflexivity Through Narrative Beginnings: Towards Living Reconciliation

2017· article· en· W2755995460 on OpenAlexafffund
Michael Dubnewick, D. Jean Clandinin, Sean Lessard, Tara-Leigh McHugh

Bibliographic record

VenueQualitative Inquiry · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsReflexivityNarrativeSituatedNarrative inquirySociologyEpistemologyPersonal narrativeAestheticsPsychologySocial scienceComputer sciencePhilosophyLinguistics

Abstract

fetched live from OpenAlex

Autobiographical narrative inquiry is an approach with a specific set of methodological commitments that guide research practice, yet its place and position within the work on reflexive practice are lost or misrepresented. Reflexivity in the form of autobiographical narrative inquiries comes out of the relational ontological commitments of narrative inquiry. By inquiring into Michael’s (the first author) experience as a researcher–practitioner, the purpose of this article is to show how reflexivity, in the form of narrative beginnings, is situated in the ongoing stream of experience. It provides narrative inquirers with avenues to make clear their research justifications/puzzles, become wakeful and open in their inquiries, and support shifts in relational knowing and being. By looking back and noticing the ways stories work on us, rather than us on them, this research explores the reverberations of past experiences and the ripples that carry forward into our future inquiries.

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)0.092
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.092
Threshold uncertainty score0.488

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.093
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0150.117
Scholarly communication0.0290.045
Open science0.0050.032
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0070.001

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 head0.592
GPT teacher head0.658
Teacher spread0.066 · 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 designTheoretical or conceptual
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

Citations21
Published2017
Admission routes2
Has abstractyes

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