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Record W2947786379 · doi:10.15402/esj.v5i2.68345

Reflecting on my Assumptions and the Realities of Arts-Based Participatory Research in an Integrated Dance Community

2019· article· en· W2947786379 on OpenAlexvenueaboutno aff
Kelsie Acton

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsDanceParticipatory action researchSociologyReflexivityCitizen journalismValue (mathematics)Transformative learningCreativityMeaning (existential)Action researchField (mathematics)Field researchVisual artsPublic relationsAestheticsPsychologyPedagogySocial scienceSocial psychologyPolitical scienceComputer scienceArtLaw

Abstract

fetched live from OpenAlex

The arts-based research paradigm prioritizes creativity, relationships and the potential of transformative change (Conrad & Beck, 2016). Arts-based research may be useful in disability communities where people may prefer to communicate artistically or through movement, rather than through spoken word (Eales & Peers, 2016). Participatory action research (PAR) involves researchers working with communities to create research critical of dominant power relations and responsive to the needs of communities (McIntyre, 2008). Both arts-based research and PAR value an axiological approach that is responsive to the community’s needs over a dogmatic procedure, meaning that researchers must be reflexive and responsive to the often unexpected realities of the field. Over four months in 2017, eight dancers/researchers from CRIPSiE (Collaborative Radically Integrated Performers Society in Edmonton), an integrated dance company, came together to investigate how integrated dancers practice elements of timing in rehearsal, through an arts-based, participatory process. In this paper I examine the gap between my assumptions of how research should be conducted and the reality of the field, specifically: the tension between university research ethics and the ethics of the CRIPSiE community, the differences between the value of the rehearsal process and the performance as sites of data collection, and the assumptions I had made about the necessity of a singular research question.

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)0.929
metaresearch head score (Gemma)0.562
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9290.562
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.4030.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.787
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 head0.732
GPT teacher head0.637
Teacher spread0.095 · 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; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
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
Published2019
Admission routes2
Has abstractyes

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