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Record W4391437807 · doi:10.14738/assrj.111.16268

Relational Arts-Based Teaching and Learning in Higher Education: Engaging the Mind, Body, and Soul of Child and Youth Care Students

2024· article· en· W4391437807 on OpenAlexaff
Ashley Brandson, Elisha A. Chambers, Mekenna Evenson, Victoria Gladue, Brenna Hein, Ryanna Laurie, Turner O’Keefe, Maggie Slaney, Gerard Bellefeuille

Bibliographic record

VenueAdvances in Social Sciences Research Journal · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSoulThe artsPsychologyPedagogyMathematics educationVisual artsArtEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This article has been written as a bridge for instructors who want to move away from conventional teaching methods but are unsure of how to do so. It profiles a relational ontological approach to teaching and learning and an arts-based theoretical framework learning assessment that child and youth care (CYC) complete for an advanced undergraduate third year CYC practice methods course. The advance methods practice course has as its focus the integration of theory, self, and ethical practice. It argues that creative experiential arts-based methods tap directly into the human experience by inviting students to attend to alternative ways of knowing and sense-making, thereby facilitating richer and deeper learning experiences by bringing to consciousness the spiritual, emotional, and mythological aspects of the self, which offers students new landscapes of reflection on themselves, their studies, and their views of the world.

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)0.003
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.008
Scholarly communication0.0060.002
Open science0.0010.011
Research integrity0.0010.003
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 head0.138
GPT teacher head0.464
Teacher spread0.326 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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