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Record W2963028253

Learning from LoLo: neighbourhood sustainable development in the City of North Vancouver

2018· article· en· W2963028253 on OpenAlexaboutno aff
Daniel Sturgeon

Bibliographic record

VenueSummit (Simon Fraser University) · 2018
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)GeographyRegional scienceEconomic geographyEnvironmental planningEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

This research investigates the integration of policies, processes and priorities in planning for sustainable development. Following case-study methodology at the neighbourhood scale in the City of North Vancouver, assessment and governance frameworks are used to understand planning for sustainable development and its outcomes. The findings uncovered rigorous planning processes that prioritized form-based planning alongside a systematic pursuit of public amenities. This was complemented with policies requiring sustainability focused items. Together these contributed to a LEED-ND comparable neighbourhood which was achieved in the absence of any 3rd party neighbourhood assessment frameworks. Other findings included: a purposively opportunistic planning practice that avoided structured assessment or monitoring; a planning process and governance arrangement that relied on a shared understanding of sustainability amongst City staff; and facilitation through leadership and a supportive political regime. The research highlights the risk and opportunity associated with the political nature of governing for sustainable development without assessment frameworks and emphasizes the importance of leadership, policy frameworks, and corporate culture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.189
Teacher spread0.179 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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