Confluency: Development of an Interactive Mobile Art Exhibit and Resource on Water Justice in South Africa and Canada
Bibliographic record
Abstract
Water justice—equitable, reliable access to clean, sufficient water, and the knowledge and mechanisms related to its management—is a key global social justice and environmental issue. Cape Town, South Africa, is an important context to explore water justice due to its 2018 water crisis. Water scarcity intersects with other issues, including health disparities, food insecurity, and gender inequity, in turn requiring citizen engagement in water-related issues and knowledge sharing to produce sustainable, contextually relevant solutions. The arts are powerful tools for citizen engagement and knowledge sharing and translation in research, as well as social and environmental action. In this Resources, Frameworks, and Perspectives article, we outline the methods and lessons learned from developing Confluency, an arts-based exhibit and resource that aimed to generate and share knowledge on water justice issues between academics, practitioners, artists, and activists in Canada and South Africa. We detail the methods used to develop the Confluency exhibit and resource, including preparing the art exhibit framework, facilitating art workshops, designing interactive stations, and implementing the interactive art exhibit. Lessons learned are shared from implementing Confluency in diverse South African and Canadian settings. These case studies signal that the methodological approaches used in designing and implementing this exhibit and resource hold promise for providing opportunities to reflect on, and learn about, global and local water justice issues. This resource could be expanded to engage communities in research, policy, and practices regarding water justice in other diverse global settings to advance health, equity, and rights.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".