Measuring progress to achieve safe drinking water for First Nations people
Bibliographic record
Abstract
In December 2021, the Community Infrastructure Target 9b was added to the National Agreement on Closing the Gap. This Target stated that First Nations’ households were to receive essential services, including “safe drinking water”, at relevant State/Territory “jurisdictional standards” by 2031. In Australia, water is considered safe if it complies with the Australian Drinking Water Guidelines (ADWG). We have proposed several potential jurisdictional/national target indicators, based on health risk. They are consistent with the ADWG advice that reducing acute (microbial) health risks is of higher priority than reducing chronic (chemical) risks. They also account for the severity of the risks involved and the number of people exposed to them. Their values were calculated from a Dataset for Indigenous communities derived from the most comprehensive annual data publicly available for smaller regional and remote communities. They show that there are many remote Indigenous communities that are facing acute health risks and many more facing severe chronic health risks. Non-compliant supplies were also prioritised for remediation, based on acute, then chronic, health risks to their corresponding communities.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".