Learning, Doing and Teaching Together: Reflecting on our Arts Based Approach to Research, Education and Activism with and for Women Living with HIV
Bibliographic record
Abstract
Body Mapping has been used for thousands of years by people who want to achieve a better understanding of themselves, their bodies and the world they live in. Artist Jane Solomon and psychologist Jonathan Morgan transformed Body Mapping for the “Long Life Project”, during the Médecins Sans Frontières (MSF) roll-out of antiretrovirals in Khayelitsha township, South Africa in 2001. Body mapping enables participants to tell their stories in the face of intense HIV/AIDS stigma. We adapted Body Mapping for the Women, Art and Criminalizaton of HIV Non-Disclosure (WATCH) study, a community arts based research (CBR) approach to better understand the impact that Canadian laws criminalizing HIV non-disclosure have on women living with HIV. Our national team includes women living with HIV, service providers, and researchers. This reflection illustrates our collective and iterative process of learning, teaching and doing body mapping workshops with women living with HIV in Canada. We share our experiences of coming to Body Mapping as an arts-based approach to CBR, how our roles as researchers stretched to include community-based education, advocacy, and group facilitation, and how we embodied the artist-researcher identity as we disseminate our research in ways that actively engage the general public on laws criminalizing HIV nondisclosure laws vis-à-vis Body Mapping galleries.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.078 | 0.082 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.007 | 0.019 |
| 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".