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Record W2898282736 · doi:10.1177/1609406918809167

Revisiting a Boy Named Jim

2018· article· en· W2898282736 on OpenAlexafffund
Katherine Bischoping

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaYork University
KeywordsReflexivityNarrativeAppropriationNarrative inquiryQualitative researchMeaning (existential)Coherence (philosophical gambling strategy)PsychologySociologyEpistemologyAestheticsLiteratureArtSocial sciencePsychotherapistPhilosophy

Abstract

fetched live from OpenAlex

Using examples from qualitative health research and from my childhood experience of reading a poem about a boy devoured by a lion (Belloc, 1907), I expand on a framework for reflexivity developed in Bischoping and Gazso (2016). This framework is unique in first synthesizing works from multidisciplinary narrative analysis research in order to arrive at common criteria for a “good” story: reportability, liveability, coherence, and fidelity. Next, each of these criteria is used to generate questions that can prompt reflexivity among qualitative researchers, regardless of whether they use narrative data or other narrative analysis strategies. These questions pertain to a broad span of issues, including appropriation, censorship, and the power to represent, using discomfort to guide insight, addressing vicarious traumatization, accommodating diverse participant populations, decolonizing ontology, and incorporating power and the social into analyses overly focused on individual meaning-making. Finally, I reflect on the affinities between narrative – in its imaginatively constructed, expressive, and open-ended qualities – and the reflexive impulse.

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)0.118
metaresearch head score (Gemma)0.066
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.297
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1180.066
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.820
GPT teacher head0.773
Teacher spread0.046 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes2
Has abstractyes

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