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Record W4399387164 · doi:10.1080/2159676x.2024.2355126

Risky methodologies and humble knowledges: a reflection on arts-based research for disabled and chronically Ill youth

2024· article· en· W4399387164 on OpenAlexaff
Fiona J. Moola, Stephanie Posa

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsCentre for Addiction and Mental HealthToronto Metropolitan UniversityToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsThe artsReflection (computer programming)SociologyPsychologyVisual artsComputer scienceArt

Abstract

fetched live from OpenAlex

The socio-materiality of risk in sport, exercise, and health has greatly advanced an understanding of the entanglement of embodiment and the social realm, opening up discussions about bodies and technologies as well as bodies and work. The socio-materiality of risk has also facilitated a discussion on both the embodied and material components of risk. In this paper, however, we consider ‘risky methodologies’ to advance discussions on the socio-materiality of risk. We unpack arts-based methodologies as one methodological platform in which bodies and the social realm are entangled and mutually constitutive of one another. Specifically, we show how arts-based research is grounded in the material and the social because it facilitates the development of ambiguous, fluid, ethereal, affective, impressionist, uncertain, and aesthetic knowledges, while, at the same time, advancing scholarship on social justice and injustice. We also propose that ABR is a humble knowledge that stands in contrast to the authority of certain academic disciplines. We weave stories, art, and narratives from empirical data sets to illuminate our discussion and promote critical methodological dialogue and reflection.

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)0.028
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.844
GPT teacher head0.676
Teacher spread0.168 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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