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Record W2768219259 · doi:10.36510/learnland.v9i2.788

Arts-Informed Narrative Inquiry: Crossing Boundaries of Research and Teaching-Learning

2016· article· en· W2768219259 on OpenAlexaffvenue
Jasna Schwind, Gail Lindsay

Bibliographic record

VenueLEARNing Landscapes · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsOntario Tech UniversityToronto Metropolitan University
Fundersnot available
KeywordsEmbodied cognitionSituatedNarrativePedagogyThe artsNarrative inquirySituated learningPsychologySociologyMathematics educationEpistemologyVisual artsComputer scienceArt

Abstract

fetched live from OpenAlex

Creative engagement accesses profound knowing and understanding that is not reachable by words alone. Situated in Connelly and Clandinin’s Narrative Inquiry, we use creative self-expression in teaching-learning, research, and practice. We examine artful approaches used in research with students and nurses in mental health, and in our classrooms. Through such artful inquiry we push the boundaries of what it means to co-create knowledge. Our students, future caregivers, learn how knowing has both epistemological and ontological dimensions. In our experience, it is embodied knowing that has the greatest potential for making connections with those in our care.

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)0.020
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.071
Scholarly communication0.0180.018
Open science0.0020.018
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.401
Teacher spread0.348 · 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
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

Citations8
Published2016
Admission routes2
Has abstractyes

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