Decolonizing research: Response art as an arts-based analysis research tool
Bibliographic record
Abstract
Indigenous scholars have consistently argued that conventional research methods perpetuate the oppression and marginalization of Indigenous communities. As we consider how certain approaches have monopolized the knowledge enterprise, a need to develop alternative ways of knowing and understanding becomes apparent. In the research project “Towards Collective Healing: Addressing the Experiences of Indigenous Women in Quebec's Provincial Prison” (2022), an arts-based analysis was conducted using the art therapy intervention known as response art (Fish, 2012). Coined by Barbara J. Fish, response art involves the creation of artwork by art therapists that responds to materials from client sessions, providing a space for reflection, emotional processing, and reaction. This paper reflects on the use of research methods using the author’s response art process in analyzing interviews conducted with community workers who support Indigenous women. Response art is rooted in decolonizing methodologies, offering researchers the opportunity to foster reciprocity through a dynamic and alternative approach to crafting knowledge and conducting research in the arts and sciences.
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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 | 0.129 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".