An Online Interactive Tool for Exploring Water Justice with Undergraduate Students
Bibliographic record
Abstract
It is vital that the next generation of public health practitioners understand the importance of ensuring affordable and equitable access to safe drinking water for all communities, and the interconnected roles that scientific research, public policy, community engagement, and advocacy play in ensuring this. Here, we describe the Water Tool, a website where student-users develop an exploratory and customizable journey through data on drinking water suppliers’ compliance with regulations, watershed pollution, and environmental justice: https://eew-sdwa-nj.streamlit.app/ In the course we built alongside a New Jersey-specific version of the Water Tool, students complete three in-class assignments and a final project. They first use it to answer a basic set of questions such as, how many public water systems are there in the state? Students then find their own water provider through an interactive map and describe the provider’s source water and number of persons served. Next, they use the tool to investigate socioeconomic, biophysical, and public health indicators of environmental inequity in their area. In the final project, students reflect on the meaning of the information they compiled and how to communicate it. Through hands-on engagement with data and structured opportunities for reflection, the Water Tool enables students to learn both about how drinking water is regulated and how to assess information on drinking water quality for specific water systems. Although we designed the tool and assignments specifically with New Jersey in mind, it could be reconfigured for use in other states or more local contexts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.027 |
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".