Analysis of challenges and opportunities to meaningful Indigenous engagement in sustainable water and wastewater management
Bibliographic record
Abstract
Access to safe drinking water and adequate sanitation continue to be significant issues affecting Indigenous populations worldwide. The full participation of Indigenous peoples within water and wastewater policy and decision-making has been hindered by many factors, including capacity, inadequate resources and, overall, a lack of respect or formal recognition of Indigenous rights. This study investigates limitations to engagement around water and wastewater management and policy. Findings from this study show that in order to improve engagement with Indigenous people on water and wastewater management policy, systemic issues need to be addressed, in addition to gaining a greater understanding of the specific socio-economic conditions, and technical and financial capacity gaps, and the recognition of inherent Indigenous rights is necessary. It is concluded that long-term sustainability of water and wastewater management necessitates Indigenous engagement from the start, as well as increased autonomy over the management of their systems, including financing. The findings from this paper can be used by policy-makers and decision-makers to address the urgent issue of access to safe drinking water and sanitation, by improving the level of engagement with community members, and challenging the status-quo of top-down approaches through community-driven processes.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".