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Record W2569356713 · doi:10.1177/0844562116684728

Moving From Field Text to Research Text in Narrative Inquiry

2016· article· en· W2569356713 on OpenAlexaffvenue
Louela Manankil‐Rankin

Bibliographic record

VenueCanadian Journal of Nursing Research · 2016
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsNipissing University
Fundersnot available
KeywordsNarrativeInterpretation (philosophy)Narrative inquiryField (mathematics)Narrative networkProcess (computing)Construct (python library)EpistemologySpace (punctuation)Personal narrativeNarrative criticismSociologyPsychologyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Narrative Inquiry is a research methodology that enables a researcher to explore experience through a metaphorical analytic three-dimensional space where time, interaction of personal and social conditions, and place make up the dimensions for working with co-participant stories. This inquiry process, analysis, and interpretation involve a series of reflective cognitive movements that make possible the reformulations that take place in the research journey. In this article, I retell the process of my inquiry in moving from field texts (data sources) to research text (interpretation of experience) in Narrative Inquiry. I draw from an inquiry on how nurses experience living their values amidst organizational change to share how I as an inquirer/researcher, moved from field texts to narrative accounts; narrative resonant threads; composite letter as the narrative of experience; personal, practical, and social justifications to construct the research text and represent it another form as a poem. These phases in the inquiry involve considerations in the analytic and interpretive process that are essential in understanding how to conduct Narrative Inquiry. Lastly and unique to my inquiry, I share how a letter can be used as an analytic device in Narrative Inquiry.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.188
GPT teacher head0.482
Teacher spread0.294 · 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

Citations16
Published2016
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Nursing ResearchSame topicCounseling, Therapy, and Family DynamicsFrench-language works237,207