Addressing water-health equity through biological engineering and theatre
Bibliographic record
Abstract
For the past decade, RESEAU has been engaging with Indigenous and rural communities across Canada in pursuit of water-health equity. RESEAU consists of a team of engineers, community partners, industry groups, and government officials working together to develop innovative solutions for water-health in small communities. Over the last six years, RESEAU has partnered with the UBC Research-based Theatre Lab to develop Treading Water, a research-based theatre play that brings to life some of the rich stories discovered during these community collaborations. The play flows between the intersecting narratives of individuals in a community dealing with unsafe drinking water and explores the resulting challenges to their health and well-being. Water operators and their experiences are central in Treading Water, and the research-based play illustrates their pivotal role in the community. This article, like the theatre initiative described, aims to open conversations addressing water quality and health issues facing rural communities in the 21st century. The article shares the collaborative process of developing the play with the various partners, the short script, as well as feedback from a performer and an evaluator. Cover image: Boil water advisory lifted. Photo credit: RESEAU
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".