Assessing asset management competency with focus on levels of service and climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it