Assessing asset management competency with focus on levels of service and climate change
Bibliographic record
Abstract
This paper aims at exploring the practice of municipal asset management (AM) planning in Ontario, Canada, and discovering the needs of municipalities. The study is based on conducting a survey of 58 municipalities, studying their AM plans and interviewing municipalities and experts. The findings show that the state of awareness and practice of AM in Ontario has progressed well. Almost all municipalities in Ontario are involved or working on AM systems, and some have reached advanced levels of AM practices. However, several issues persist. Capacity building is at the core of the gaps. There is a need for training professionals on AM concepts and tools. Also, providing guidelines and support for change management in decision-making practices is needed. Smaller municipalities are still facing issues defining the levels of service and linking them to decision making. The next need for AM in Ontario is to link it to climate change strategies and programmes. While some of the municipalities are aware of climate change, they mostly have taken no practical steps regarding adaptation or mitigation. Among key challenges to the success of the Ontario AM strategy is the management of data. There is inconsistency in the specifications for data and limited quality assessment or interoperability guidelines.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".