Teaching and Knowing beyond the Water Cycle: What Does It Mean to Be Water Literate?
Bibliographic record
Abstract
Water is an extraordinary thing: it is the key to the chemistry of life. If it wasn’t for water’s unique properties, such as its abilities to dissolve other substances, life could not exist on our planet. Indeed, life was thought to have started in water and currently more than half of the plant and animal species live in water. On land, plants and animals need water for their existence, as the ability of water to disassemble and rearrange other molecules is essential to all daily actions. As humans, our bodies consist of about 80% water when we are babies, to around 60% - 65% as adults. The human brain is about 85% water. Even though this simple polar molecule is one of the most prized possessions in the universe, what do people know about water? What does it mean to be water literate? In this paper, we explore what it means to be water literate in the fields of engineering and in science education. We will compare this theoretical understanding with what engineering and science education students actually know about water. We finish with recommendations to increase student’s literacy in water.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".