Investigating the Commitment to Official Plan Monitoring and Evaluation in Ontario’s Mid-Sized Cities
Bibliographic record
Abstract
Plans often need updates to stay applicable to evolving needs, experiences and knowledge. In this context, monitoring and evaluation of plans has a critical role to play in guaranteeing the applicability and relevance of the plans. Monitoring and evaluation can provide planners with information which can help them make decisions based evidence of plan performance. However, despite its significance and potential, monitoring and evaluation is generally a forgotten step in planning practice. \nVery little research has been done about the municipal experience with comprehensive community plan monitoring and evaluation in Canada or elsewhere. Therefore, the aim of this research is to identify whether and to what extent mid-sized municipalities in Ontario are evaluating their Official Plans and to compare and contrast the current practice with the best practices described in the literature. A sample of mid-sized cities in Ontario has been selected for this research because this cohort has received a very little attention in the planning literature, generally. \nTo conduct the research, various parameters that constitute an ideal plan monitoring and evaluation were identified from the literature review. The Official Plans and other major monitoring reports including housing and environmental monitoring reports were content analysed to identify the state of Official Plan evaluation in the mid-sized cities. The findings of the research suggest that there is a significant gap between what may be considered the best practices for plan monitoring and evaluation as mentioned in the literature, and the reality in the mid-sized municipalities in Ontario. Municipalities monitor progress made only in some specific policies such as housing/residential policies, growth management policies and to some extent environmental policies. Furthermore, the writing and structure of plans does not facilitate monitoring and evaluation. \nTherefore, to strengthen plan evaluation practice in mid-sized cities of Ontario, the study recommend the provision of monitoring and evaluation guidelines from the provincial government, building institutional capacity, the formulation of evaluable/quantifiable policies, enhancing the use of outcome-focused indicators, and writing Official Plans in a way that facilitates evaluation.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".