Multilevel performance management framework for small to medium sized water utilities in Canada
Bibliographic record
Abstract
In Canada, small to medium sized water utilities (SMWU) do not often participate in National Water and Wastewater Benchmarking Initiative due to lesser economies of scale, lack of resources, and data limitations. Consequently, such SMWU are managing their functional components (i.e., environmental, personnel, operational, physical assets, customer satisfaction, public health, and financial) without quantitatively assessing and knowing if they are meeting their performance objectives. A multilevel performance management framework, consisting of five modules has been developed and implemented for SMWU in BC, Canada. The framework provides an approach to identify and select the suitable performance indicators for SMWU, and to use them for inter-utility performance benchmarking under limited data. The subsequent modules can be used for detailed performance management at utility, system, and sub-component levels. The utility managers can effectively employ this framework to identify the underperforming functional components and can rationally take corrective actions, and address customer satisfaction with efficient inventory management and data analyses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".