Standardizing Ontario’s Permitting Process for E-Permitting Implementation
Bibliographic record
Abstract
As municipalities across Ontario grow and densify, they ought to deal with an increase in both the number and complexity of building permits. These municipalities are starting to take advantage of recent permitting technologies by migrating towards electronic permitting (e-permitting) systems. E-permitting systems have been shown to increase the efficiency of the permitting process, allowing for faster processing of permits, providing added transparency to the approval process, and streamlining document reviews and revisions. More advanced e-permitting systems are able to take advantage of latest technological advancements including building information models (BIM) and geographic information systems (GIS) to enable intelligent city planning and city management capacities. The fragmented nature of the 440 municipalities in Ontario and the lack of a standards process, along with inefficient internal and external data exchanges, are major obstacles in progressing towards e-permitting systems. In this research, the challenges regarding process and data exchange standardization will be examined and will be complimented with a case study which will review the permitting process of an Ontarian municipality. This research can further be utilized as a valuable resource for other municipalities who wish to adopt an e-permitting platform.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".