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Record W3159657230 · doi:10.46743/2160-3715/2021.4371

Reflexivity Through a Yoga Class Experience: Preparing for My Health Promotion Without Borders Excursion to Mongolia

2021· article· en· W3159657230 on OpenAlexaff
Shelby Deibert, Stephen D. Ritchie, Bruce Oddson, Ginette Michel, Emily J. Tetzlaff

Bibliographic record

VenueThe Qualitative Report · 2021
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsLaurentian UniversityMcMaster University
Fundersnot available
KeywordsAutoethnographyReflexivityNarrativePsychologyTheme (computing)Argument (complex analysis)Class (philosophy)SelfSociologyAestheticsSocial psychologyEpistemologyMedicineGender studiesComputer scienceArt

Abstract

fetched live from OpenAlex

In this paper, I (S. L. Deibert) share my story of discovering the relationship between reflexivity, autoethnography, and yoga through a meaningful experience. Yoga has been an important influence on my physical and mental well-being for over a decade, but I did not consider its implications in my academic life until I was asked to write a reflexive assignment for a course. The task was exploring who I am in connection to my master’s thesis project; the challenge was finding a starting point for my reflexive journey of self-discovery. Frustrated by the latter, I turned to yoga for refuge; instead of escaping the assignment, I found that my quest for self-exploration was intertwined with my yoga practice. The purpose of this paper is to delve further into my experience with yoga as a medium for developing reflexivity. Using autoethnography, I share my journey of developing critical thinking through a narrative related to my yoga class experience. Linking my research to my yoga practice allowed me to better understand myself as a person and researcher, become mindful of how my own views shape my experiences, and develop a deeper level of critical reflection. Overall, this work demonstrates the experience of a connection between yoga, reflexivity, and autoethnography, and adds to the sparse literature exploring the intersection of these three.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.234
GPT teacher head0.601
Teacher spread0.367 · 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.

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
Published2021
Admission routes1
Has abstractyes

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