Tapping into Consumers' Perceptions of Drinking Water Quality in Canada: Capturing Customer Demand to Assist in Better Management of Water Resources
Bibliographic record
Abstract
Canadian municipal water utilities have had to face many difficulties in the past few years: increasing water treatment and processing costs, tighter fiscal constraints, changing regulations regarding water quality, and aging and rapidly deteriorating infrastructure. Not the least of these problems has been an erosion of consumer confidence in the reliability and safety of publicly-supplied tap water. Many consumers have "voted with their feet" by choosing to install in-home water filtration devices or to purchase bottled water. This paper reviews results from Canadian surveys on perceptions of the quality of municipally supplied tap water. Next, it examines the approach to water management adopted by the United Kingdom (UK) over the last 15 years. This examination provides valuable lessons to Canada's policy makers to encourage them to adopt integrated water resources management (IWRM). In particular, the paper argues that water utility performance can be enhanced by applying one of the most fundamental "economic instruments", namely the use of information about consumer preferences. In so doing, water utilities promote IWRM. This should result in more satisfied customers and a more efficient use of Canada's scarce water resources. The key challenge for regulators will be the design of incentive mechanisms that encourage Canadian water utilities to adopt information gathering practices similar to what is currently done in the UK.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".