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Record W4310249400 · doi:10.1177/25148486221140878

Planning use values or values-based planning? “Rolling with” neoliberal flood risk governance in Vancouver, Canada

2022· article· en· W4310249400 on OpenAlexafffundabout
Greg Oulahen, Jacob Ventura

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsToronto Metropolitan University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsFlood mythCorporate governanceRisk governanceContext (archaeology)Environmental planningUrban planningGeographyPolitical scienceEnvironmental resource managementBusinessEconomicsCivil engineeringFinanceEngineering

Abstract

fetched live from OpenAlex

Neoliberal flood risk governance has become the norm in Canada and much of the rest of the global North in the interest, hypothetically, of achieving so-called efficiencies and resilience and, practically, out of desperation for access to any more resources to face a growing burden. This paper traces the path toward a flood risk governance model in Vancouver together with the development history of the city to illustrate the coproduction of urban landscape, capital, and flood risk. It situates what is intended to be a progressive “values-based” local adaptation planning program within that context to question whether or not such a program can elevate the use value of land. The paper demonstrates that a flood risk governance model further entrenches neoliberal hegemony and exchange values, with implications for urban space and how city inhabitants interact with flood hazards that are beyond the reach of values-based planning.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.872

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.011
Scholarly communication0.0080.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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