Bibliographic record
Abstract
Water demand management attempts to balance the supply of and demand for water by controlling the competing water demands. It realizes the change by influencing peoples' behavior with respect to water use. Water demand management (WDM) is necessary in water scarce regions like Barbados. The Government of Barbados has recognized the need for WDM as demonstrated by the implementation of measures such as universal metering and water pricing. This research looks at the impact of water pricing and metering on residential water use in Barbados. Econometric demand models of residential water use are developed to assess the potential of pricing policies to conserve water. Price elasticities between -0.18 and -0.93 were obtained suggesting that pricing policies can be used to reduce and control residential water consumption in Barbados. The results of the models are then used to investigate the impact of different rate structures on water use and revenue generation. It is predicted that a 26% decrease in water demand and a 52% increase in revenue collected from water bills would be achieved if the 1997 proposed water rate increase is implemented. In addition, results indicate that water production decreased by 12% from 1997 to 2000, coinciding with the implementation of the Universal Metering Program. However, per-capita consumption has been on the rise in recent years suggesting that metering must be accompanied by a substantial increase in price to encourage water conservation.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".