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Record W4411249572 · doi:10.1177/08861099251348201

Finding Freedom and Connection in Arts-Based Collaborative Autoethnography

2025· article· en· W4411249572 on OpenAlexaff
Bridget M. Colacchio, Darren Cosgrove, Dana S. Levin

Bibliographic record

VenueAffilia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAutoethnographyThe artsConnection (principal bundle)SociologyPsychoanalysisVisual artsAestheticsPsychologyArtGender studiesMathematicsGeometry

Abstract

fetched live from OpenAlex

Drawing upon embodied and feminist knowledge, arts-based research (ABR) is a methodological approach aimed at advancing work that is liberatory both in its findings and its processes. To examine this phenomenon, three social work scholars engaged in a reflective research process to study the meaning of arts-based research through a methodology blending arts, collaborative autoethnography (CAE) and phenomenology. The team explored our own experiences of using art in research, and the implications for feminist social work. This article begins with a brief overview of ABR before introducing the strategic use of art creation and discourse within our CAE. We present the findings uncovered through analysis of our qualitative data - including our own artwork - and the experiences they represent. Thematic findings include: power dynamics in research and academia as revealed through ABR, and the liberatory components of ABR. Lastly, we discuss the implications of the findings, strengths, and limitations, and suggest future research. Implications for social work practice and research include: opportunities for integrating humanistic and engaged praxis as tools of liberatory knowledge production; the promotion of feminist-based inquiry as resistance to social pressures; and the use of ABR as an anti-oppressive research approach.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.418
GPT teacher head0.617
Teacher spread0.199 · 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 designObservational
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

Citations2
Published2025
Admission routes1
Has abstractyes

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