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Record W2921084548 · doi:10.14288/1.0373602

Towards urban and regional resilience : a case study of Metro Vancouver region, Canada

2018· article· en· W2921084548 on OpenAlexaboutno aff
Lilia Yumagulova

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)GeographyEnvironmental planningMetropolitan areaUrban resilienceRegional scienceUrban planningCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Climate change necessitates investments in urban and regional resilience to address existing and novel risks. Drawing on disaster resilience, socio-ecological resilience, planning, and institutional adaptation literatures, I investigate the relationship between the institutional capacity to deal with floods (specific resilience, sp-R) and the overall ability to deal with change (general resilience, gen-R). I make a theoretical and empirical contribution by operationalizing the interdisciplinary sp-R–gen-R framework and testing it in Canada’s Metro Vancouver region. I employ a nested comparative case-study design, drawing on 60 interviews (with engineers, planners, emergency managers, policy-makers, and politicians) and a regional expert survey to analyze the institutional responses to existing and future flood risk by answering the following question: What is the relationship between sp-R and gen-R across governance scales? Overall, sp-R and gen-R are related through feedback loops across spatial, temporal, and jurisdictional scales, enabled by champions and social capital, subject to political and institutional changes within the governance system and bureaucracy. Municipally, sp-R and gen-R are related through urban planning processes and outcomes, organizational dimensions, and decision-making processes. Carefully constructed planning processes and nurtured organizational cultures enhance trust, shorten feedback loops between the politicians and bureaucracy, foster sp-R action and enhance gen-R. Municipal sp-R mechanisms (practices, tools, and innovations) reflect gen-R principles such as diversity and modularity but also present a governance/coordination challenge. By emphasizing site/area specific tools, municipal-scale planning mechanisms reduce opportunities for strategic region-wide flood management. While numerous municipal-scale sp-R¬–gen-R trade-offs (e.g., fiscal, equity, design) exist, regional decision-making mechanisms for addressing the cumulative effects of municipal sp-R responses are lacking. Provincially, the eroded gen-R limits municipal and regional sp-R options. Regional sp-R is constrained by multi-scalar barriers including regional-level flood risk governance regime gaps, a lack of federal and provincial leadership, and provincial sp-R path-dependencies (e.g., competitive funding arrangements that favour structural approaches). Moving from government to governance, regional sp-R planning is driven by champions and boundary organizations, which fosters gen-R through learning, collaboration, and exploration of options. Implementation of these options will require support and engagement from higher government levels and wider governance actors.

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 score0.053
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0210.004
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.176
Teacher spread0.158 · 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
Published2018
Admission routes1
Has abstractyes

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