Use of Arts-based Research to Uncover Racism
Bibliographic record
Abstract
The article provides an overview of arts-based research (ABR) within social work and general healthcare practice in Canada, and how it can be used to uncover racism within vulnerable populations, particularly youth, women, immigrants and refugees, the lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI) community, and Indigenous peoples. This is a general review of the literature. A literature search was conducted using the University of Western Ontario’s Summons database, with coverage from January 2000 to February 2019. Data exploring participant experiences, personal identity, voice, and invisible powers were extracted, and analyzed using a critical race lens to examine the intersection of societal and cultural practice with race and power.Results indicate that ABR can support therapeutic recovery from oppression by enhancing self-expression of feelings and thoughts, and affording participants the agency to reclaim and reframe their personal narrative. ABR can further generate a sense of community by creating connections between participants with similar oppressions to overcome disconnection and marginalization. Within a broader community context, ABR permits the sharing of stories and insights with others, which can generate dialogue on important social issues to expose areas of social inequity and oppression alongside potential solutions for transformative social action. This dialogue can also extend to discussions with policy makers on the impact of social inequities to guide recommendations that address system gaps for broader community-level change. The paper concludes that ABR can move beyond merely reflecting on social conditions toward actively addressing them by promoting sustainable social change. The voices expressed through ABR illustrate possible solutions to overcome racism through inclusive social practice, deconstruction of the racial status quo, and movement toward an equitable distribution of power.
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 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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.015 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".