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Record W2322621497 · doi:10.1177/1541344612441083

Arts-Based Critical Inquiry in Nursing and Interdisciplinary Professional Education

2011· article· en· W2322621497 on OpenAlexaff
Judith A. MacDonnell, Geraldine Macdonald

Bibliographic record

VenueJournal of Transformative Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsTransformative learningExperiential learningPedagogySociologyThe artsContext (archaeology)Psychology

Abstract

fetched live from OpenAlex

In this article, transformative educators are encouraged to use an arts-based critical inquiry approach within their diversity-sensitive teaching practice. Using a Socratic dialogue, the authors describe how guided imagery, images, narratives, and poetry have been useful in developing transformative insights in relation to spiritual/ecological values, sexual orientation, and culture in the context of nursing and interdisciplinary professional education. Arts-based approaches that incorporate constructivist dimensions can potentially stimulate critical inquiry in educational settings that can spark the thoughtful dialogue and passion needed for practitioners to engage in emancipatory practices for social change. The underlying adult learning theoretical frameworks for this teaching and learning dialogue are transformative learning and unlearning. Innovative arts-based and experiential approaches to diversity-sensitive teaching nurture the dynamic engagement and individual and collective reflection that fosters interpersonal understanding and hope for meaningful community connections and social change.

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)0.029
metaresearch head score (Gemma)0.035
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 score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.002
Science and technology studies0.0080.063
Scholarly communication0.0150.009
Open science0.0020.010
Research integrity0.0040.005
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 head0.056
GPT teacher head0.472
Teacher spread0.417 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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