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Record W6964189038 · doi:10.25316/ir-19123

A midway between public and private: Exploring elements contributing to efficient and effective rezoning process on Vancouver Island

2023· dissertation· en· W6964189038 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Private sectorConstruct (python library)Public sectorKey (lock)Public use

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.241
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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