Advancing water justice through a tribally-driven partnership: Designing sustainable rainwater harvesting systems in the Yukon–Kuskokwim delta of Alaska
Bibliographic record
Abstract
Community driven co-design models can help collaborators to respectfully engage in projects that provide much-needed resources and services to underserved communities. For example, partnerships between tribal, academic, and non-profit collaborators have the potential to generate positive outcomes for communities when individual efforts by those same groups may be less successful. However, cultural and spiritual differences between collaborators (particularly tribal and non-tribal) can lead to misunderstandings and negative project outcomes, despite good intentions and an honest effort by collaborators to achieve a common goal. Here, we provide a case study of a community-driven co-design project involving tribal, academic, and private collaborators to design and build a rainwater harvesting system with the Akiak Native Community (ANC), and their tribal council in Alaska, USA. A novel collaborative co-design process honoring the tribal sovereignty of the ANC is emphasized in this case study; a design model that is poorly represented in the literature with real-world examples. Logistics associated with designing and constructing the community-use rainwater harvesting system on Alaskan tribal lands is reviewed but the focus of this work is on the collaborative design process more so than the construction of the water harvesting system end product. More explicitly, the use of multiple approaches to promote collaborator involvement along with an emphasis on developing community driven project goals are highlighted as essential steps in our co-design process.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".