The effects of racism, social exclusion, and discrimination on achieving universal safe water and sanitation in high-income countries
Bibliographic record
Abstract
Drinking water and sanitation services in high-income countries typically bring widespread health and other benefits to their populations. Yet gaps in this essential public health infrastructure persist, driven by structural inequalities, racism, poverty, housing instability, migration, climate change, insufficient continued investment, and poor planning. Although the burden of disease attributable to these gaps is mostly uncharacterised in high-income settings, case studies from marginalised communities and data from targeted studies of microbial and chemical contaminants underscore the need for continued investment to realise the human rights to water and sanitation. Delivering on these rights requires: applying a systems approach to the problems; accessible, disaggregated data; new approaches to service provision that centre communities and groups without consistent access; and actionable policies that recognise safe water and sanitation provision as an obligation of government, regardless of factors such as race, ethnicity, gender, ability to pay, citizenship status, disability, land tenure, or property 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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".