The Successful Implementation of O. Reg. 588/17: Organizational Factors that Contribute to the Success of Asset Management Planning for Municipal Infrastructure in Small Ontario Municipalities
Bibliographic record
Abstract
Asset management planning in Ontario has evolved since the early 2000s and Ontario Regulation 588/17: Asset Management Planning for Municipal Infrastructure came into effect on January 1, 2019. The requirements of the regulation are different for small municipalities, being those having a population of less than 25,000, than for large municipalities. The burden of undertaking large-scale change initiatives, specifically those that are externally initiated, can be especially difficult for small municipalities that often lack the resources and specialized staff to address these changes. This paper explores the organizational factors that contribute to the success of local government asset management planning activities in small municipalities. A review of asset management and change management literature determines the elements that contribute to successful change management in public organizations undertaking asset management programs. Central to this is review is a change management framework consisting of the eight factors necessary for successful change in public organizations that was developed by Fernandez and Rainey in 2006. Through this research, the framework is adjusted to remove extraneous factors and add factors central to the asset management literature. The factors found to contribute to the successful delivery of asset management programs in small municipalities are as follows: Factor 1 – Provide and Implement a Plan Factor 2 – Ensure Top Management Support and Commitment Factor 3 – Use Evidence-Based Decision-Making\t Factor 4 – Institutionalize Change Factor 5 – Provide Resources and Pursue Comprehensive Change Factor 6 – Build Internal Support by Communicating Strategically The applicability of these six factors is then validated through a longitudinal comparison case study. The case study demonstrates how Loyalist Township, a small municipality in southeastern Ontario, has employed these methods to contribute to the success of its asset management planning over a 10-year period, from 2009 to 2019.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".