Governance, business models and restructuring water supply utilities: recent developments in Ontario, Canada
Bibliographic record
Abstract
Many municipal governments are currently confronted with the need to restructure water supply systems. This paper examines how municipalities are restructuring water supply utility management in the province of Ontario (Canada), which has recently experienced significant and rapid legislative and regulatory reform in the water sector. The paper analyses restructuring in six different municipalities (Hamilton, Kingston, Peel, Peterborough, Toronto and York). It identifies six distinct business models adopted as an outcome of the restructuring process (delegated management to a private operator, corporatization of services provision, delegated management to a public operator, a municipal commission, a municipal ‘business unit’ and a municipal department) and examines the different approaches to governance adopted during the restructuring process. The case study is conceptualized through a discussion of the governance and restructuring challenges faced by municipalities. As municipalities are often confronted with a bewildering array of business models, governance frameworks and contract types when engaging in a review of restructuring options, the paper situates the analysis of the Ontario case within a general survey of business models for networked water supply. The paper concludes with a discussion of “lessons learnt” relevant to municipalities and higher orders of government when engaging in restructuring of networked water supply provision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 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".