Finding Freedom and Connection in Arts-Based Collaborative Autoethnography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".