Cultivating Community Care: Using Research-Creation & Art-Based Workshops to Explore Care in Queer and Mad/Disabled Communities in Toronto
Bibliographic record
Abstract
Drawing on six art-based workshops and focus group sessions that took place from January to February 2023 with six 2SLGBTQ+ and/or Mad/Disabled identified participants from Toronto, Ontario, my thesis reimagines and redefines (community) care from a queer/mad/disabled perspective. Drawing on a Research-Creation informed visual methodology for community- and art-based research, this project challenges traditional ideas around knowledge production in the academy by inviting 2SLGBTQ+ and Mad/Disabled participants into the knowledge creation process through arts-based community research. I audio recorded, transcribed, and analyzed art workshops and focus group sessions using applied thematic analysis to identify themes emerging from workshop and focus group sessions. I then grouped these research findings thematically into narratives of care I identified in the transcripts. In my Findings section, I identify several care frameworks and core features of community care that participants described as essential to meeting their care needs. As a collaborative community-based research project between my participants and I, this project contributes to academic discourse on care in queer and Mad/Disabled communities.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.030 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".