Co-designing for equity – undesigning anti-Black racism in the arts in Canada
Bibliographic record
Abstract
The Make Some Noise: Hidden Stories of Black Creatives in Canada was a co-design research study with Black creatives across Canada representing diverse disciplines of design, visual and performing arts. Participants self-identified as emerging, mid-career, or established in their practice. The study mapped the ecosystem across Canada of barriers and facilitators influencing Black creatives’ career progression. Sixty-eight Black creatives participated in 10 virtual co-design research consultations. Two speculative co-design sessions were held with 22 participants to inform the design of a resource tool to support their creative practice, strengthen their creativity and overcome reported barriers. Participants co-designed the characteristics, form, and key functions of the tool and speculated on how the tool would be accessed and used, now and in the future. Key themes that emerged from the stories they shared of their lived experience of institutional anti-Black racism were: (1) protective factors and empowerment, (2) creating opportunities for ourselves, (3) overcoming institutional barriers, (4) finding creative solutions: opportunities, successes, economic sustainability, and (5) resources needs. This study contributes to the discourse on design’s role in fostering positive societal change when participants with lived experience are recognised as subject matter experts and empowered to be agents of change.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.049 | 0.041 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".