A midway between public and private: Exploring elements contributing to efficient and effective rezoning process on Vancouver Island
Bibliographic record
Abstract
This thesis explores the elements that contribute to the efficiency of the Rezoning Approvals Process within the public and private sectors on Vancouver Island. The efficiency of the rezoning process is a contributing factor to the cost of housing and the presence of conflict in development approvals. Relevant literature along with other secondary data sources was analyzed to formulate a framework of issues outlining the key elements of the rezoning approvals process. The framework focused on how the rezoning process can be assessed based on the local government's need to regulate the growth according to municipal plans and policies, and the developer's need to construct financially feasible projects. Online interviews workshops were conducted with municipal planners, private developers, First Nation managers, and technical consultants across Vancouver Island, to understand the challenges in the rezoning approvals process, administrative culture and capture their insight regarding the improvement of the process. The findings from this study provide various recommendations for the public as well as the private sector focusing to make the overall process efficient and effective. This research project acts as a baseline in the dialogues that gravitated towards streamlining the development approvals process and welcomes future researchers to strengthen the ideas put forward.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".