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Record W3035310253

Multilevel governing in British Columbia: A case study of residential development and the Agricultural Land Reserve in the City of Richmond

2020· article· en· W3035310253 on OpenAlexaboutno aff
Sara Obidi

Bibliographic record

VenueSummit (Simon Fraser University) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyAgricultureArchaeologyAgricultural economicsEconomics
DOInot available

Abstract

fetched live from OpenAlex

This thesis presents a locally specific case study of the Agricultural Land Reserve (ALR) in the City of Richmond, British Columbia, providing an examination of multi-level governance and government 'on the ground' in Canada.The last several years represents a significant period of policy and political change, at both the City of Richmond and the Province of British Columbia, intended to protect ALR land from residential and accessory residential uses as well as the outright exclusion of land from the Reserve.Yet, a lack a cooperation and policy coordination between, across and within federal, provincial, regional, and municipal scales has allowed such exclusions and the increased residential and accessory residential development of land within the ALR to occur.Such policy discord and inconsistencies are largely attributable to several challenges inherent in the multi-jurisdictional character of the ALR with sometimes competing and conflicting interests between government scales and conflicting private and public interests.Most significant has been a lack of political will to act and the passing off of jurisdictional responsibility between government levels.Moving forward, further province-wide regulation limiting non-agricultural uses of ALR lands while allowing for continued municipal flexibility in regulating below these provincial benchmarks is needed.Such increased provincial regulation would allow for greater consistency between municipalities as well as urban and agricultural areas within cities, reducing the appeal of ALR lands for residential and accessory residential development.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0190.005
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.215
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2020
Admission routes1
Has abstractyes

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