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Record W4391110801 · doi:10.1177/10778004231219806

Embodied Reflexivity Through the Arts: An Introduction

2024· article· en· W4391110801 on OpenAlexaff
Ellyn Lyle, Jee Yeon Ryu, Celeste Snowber

Bibliographic record

VenueQualitative Inquiry · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsSimon Fraser UniversityYorkville UniversityCape Breton University
Fundersnot available
KeywordsReflexivityEmbodied cognitionSociologyAutoethnographyAestheticsConsciousnessCritical consciousnessThe artsAlienationEpistemologyPedagogyVisual artsSocial sciencePolitical science

Abstract

fetched live from OpenAlex

Emerging from a global health crisis that shone the light on the effects of alienation, isolation, and physical and spiritual vulnerability, we wondered what we might offer to re/center humanness and remind us of individual and collective possibilities to create positive change. With this call in our hearts and bodies, our special issue began to take shape. We are three scholars at different stages of our careers and with intersecting areas of expertise, but who are all grounded in reflexive ways of being, embodied ways of knowing, and artful ways of engaging. We acknowledge the scholars(hip) in each of these distinct areas and are grateful for the foundation on which we build. Thus, with our collective yearnings to centre an integrated way of at/tending to an emergent process of re/making and re/imagining knowledge, we offer this special issue with three intentions: to advance arts-based educational research in developing critical, social, and relational consciousness; to evoke/provoke embodied pedagogical practices that transform teaching/learning; and to contribute to re/humanizing education (Lyle, 2022) and social justice reform.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0060.030
Scholarly communication0.0130.011
Open science0.0020.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.335
GPT teacher head0.454
Teacher spread0.119 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2024
Admission routes1
Has abstractyes

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