Perceptions of Water Ownership, Water Management, and the Responsibility of Providing Clean Water
Bibliographic record
Abstract
Perceptions of water and water related issues still render many under-researched topics. This study aims to further our knowledge regarding people’s perceptions of water and our understanding about the different ways individuals use water. The authors asked the question: Does the way an individual perceives water (i.e., as a commodity, a human right, private resource, public resource and/or natural resource) influence consumption and conservation of water, and sentiments towards control and allocation of water? An exploratory online questionnaire was designed to generate qualitative and quantitative data of survey participants’ perceptions, beliefs and actions towards water issues, such as overconsumption and scarcity. Data analysis included comparison of the quantitative data regarding the non-statistical association between how an individual perceives water and the individual’s beliefs, as well as qualitative analysis of the comments using an iterative pattern coding technique. One hundred and sixty four individuals participated in the survey (75% completion rate) and over 430 comments were made. Themes that emerged from the comments included: responsibility, scarcity, the value of water, knowledge gained and education needed. Comparison of the different perceptions of water revealed that different perceptions of what water is resulted in different beliefs about what the cost of water should be. These findings have implications for future water use, including what needs to change in order to increase appreciation for water issues.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".