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Record W3203510513 · doi:10.1177/15356841211046265

Development, Responsibility, and the Creation of Urban Hazard Risk

2021· article· en· W3203510513 on OpenAlexafffundabout
Timothy J. Haney

Bibliographic record

VenueCity and Community · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMount Royal University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFlood mythWork (physics)RedevelopmentHazardSociologyUrban planningGovernment (linguistics)Public relationsRisk perceptionEnvironmental planningPolitical sciencePerceptionCivil engineeringLawEngineeringGeographyPsychology

Abstract

fetched live from OpenAlex

Scholarly attention has recently shifted to the creation and redevelopment of urban hazardscapes. This body of work demonstrates how housing is deployed in close proximity to hazards, and how the attendant risks have been communicated-or not-to potential residents. Utilizing the case of Calgary, Alberta, this article uses interview data collected from flood-impacted residents, and looks at their perceptions of development and risk creation. The analyses focus on how people attribute responsibility for development in flood-prone areas, and their views on future development in these areas. Results reveal that many residents argued for more government regulations preventing new development in floodplains. Moreover, they viewed developers as narrow-interested capitalists who fail to protect public safety and work to conceal risk from the public. Others wished to see large structural mitigation projects-dams, levees, or floodwalls-or insisted that homebuyers be informed of flood risk prior to purchase. The article concludes by addressing the implications for scholarly work in urban sociology, environmental sociology, and the sociology of disaster-all of which grapple with tensions between place-making and risk creation.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.034
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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 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

Citations5
Published2021
Admission routes3
Has abstractyes

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