Exploring, Understanding, and Determining the Quality of Plan Monitoring and Evaluation in Ontario
Bibliographic record
Abstract
Planning takes place in the context of diverse stakeholder interests and complex decision-making processes. Planning activities are often complicated by a range of factors: increasing pressure on municipalities with ageing populations and infrastructure; the downward transfer of responsibilities from higher levels of government to lower ones; budget and resource constraints; increasing pressure on the provision of infrastructure services; political change; climate change and environmental issues; urban sprawl and gentrification; and limited availability of skilled labor. These factors have intensified the challenge of making sound and optimal decisions that consider the interests of diverse stakeholders. \n \nTo improve decision-making, planners invest significant resources in the creation of plans and policies. It thus becomes important to consider whether planning decisions and interventions align with the visions, goals, objectives, and targets crafted within these plans. Plan monitoring and evaluation helps to track the performance of planning actions and considers the alignment of these actions with pre-defined goals, objectives, and targets. A significant amount of research has investigated the efficacy of plan monitoring and evaluation; this research has determined that monitoring and evaluation remains an undervalued or forgotten step of the planning process. \n \nA mixed-methods research approach was adopted to explore the efficacy and quality of various plans and reports in terms of plan monitoring and evaluation. A combination of qualitative review and content analysis exercises determined that the overall quality of the plans and reports with regard to monitoring and evaluation is quite far from the ideal plan monitoring and evaluation practice defined by the literature. A set of parameters deemed important for high-quality plans and reports was identified as part of the literature review. Using these parameters, the plans and reports were analyzed (quantitatively). \n \nIt was observed that all the municipalities being investigated do engage in monitoring and evaluation, but to different degrees with some municipalities demonstrating closer alignment with the principles of ideal monitoring and evaluation practice than others. Almost all the municipalities investigated consider monitoring and evaluation to be necessary activities, but more work needs to be done to integrate plan monitoring and evaluation into the plan-making process to ensure that these practices are considered as part of the design and drafting of plans. Findings also indicate that provincial mandates have implications for how municipalities perceive monitoring and evaluation. These findings point to the need for fundamental guidance from the Province regarding plan monitoring and evaluation. \n \nFurther, it was observed that lack of plan monitoring and evaluation can be attributed to less visible factors including organizational attitudes, political realities, and awareness and education among existing and future planners. Thus, it is crucial to define the role of professional institutions like CPI and OPPI, the role of education institutions such as universities, and the role of ministry and provincial planners with regard to awareness-raising, education, and capacity building. Finally, there is an urgent need to educate planners and enhance their capacity to improve the current state of plan monitoring and evaluation.
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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 | 0.113 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".