Community Improvement Plans: An analysis of content and outcomes of Community Improvement Plans in Ontario
Bibliographic record
Abstract
Community Improvement Plans are an often used, but seldom studied economic development and planning tool used to spur economic growth within an economically depressed area. But are they effective? This paper answers two questions concerning Community Improvement Plans. First, “How are Community Improvement Plans used in the Province of Ontario?”; and second, “Is the execution of these plans resulting in greater economic prosperity for the communities that have enacted them?”. This work is undertaken by reviewing the content of 202 Community Improvement Plans across the Province of Ontario. Regression tests on municipal assessments from 2001-2018 to determine what effect the adoption of Community Improvement Plans has on assessment values. This paper highlights differences in Community Improvement Plan usage according to the regions they were written in, the size of municipality that has enacted them, and nature of their authorship. It also seeks to use changes in municipal assessment over time as a measure for the effectiveness of Community Improvement Plans. Although many of the outcomes are statistically significant, it is determined that changes in assessment are tied too closely to the size of a municipal population to be an effective tool to measure the effect of Community Improvement Plans
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".