Analysis of Municipal Permitting Systems and Trends in Preparation for Electronic Permitting Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ontario municipalities have complex permitting systems that have been shown to result in lengthy delays. These delays have been getting worse over the years, and municipalities are looking for ways to reduce them. Based on international experience, electronic permitting (e-permitting) has been shown to improve efficiency, and many municipalities in Ontario are looking to adopt such technologies and processes. To do this, it is important to evaluate the current permitting landscape in the province and identify what municipalities need to consider before adoption. This thesis investigates the permitting trends that Ontario municipalities are experiencing and explores the economic circumstances that may impact these trends. Additionally, the permitting data of an Ontario municipality is thoroughly analyzed to identify potential issues in permitting timelines. Finally, a municipality’s plans review process is scrutinized to develop a series of considerations for municipalities interested in e-permitting.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 it