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Record W2034439512 · doi:10.1515/1944-4079.1098

Citizen Participation in Post‐disaster Flood Hazard Mitigation Planning in Peterborough, Ontario, Canada

2012· article· en· W2034439512 on OpenAlexaffabout
Greg Oulahen, Brent Doberstein

Bibliographic record

VenueRisk Hazards & Crisis in Public Policy · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of WaterlooWestern University
Fundersnot available
KeywordsFlood mythGovernment (linguistics)HazardEnvironmental planningStrategic planningEmergency managementFlood mitigationSettlement (finance)Public administrationPolitical scienceBusinessManagementGeographyFinanceEconomics

Abstract

fetched live from OpenAlex

Abstract Citizen participation is recognized as a standard feature of democratic planning. This article examines the role of citizen participation in a post‐disaster flood hazard mitigation planning program in Peterborough. The “six strategic planning choices” outlined by Brody, Godschalk, and Burby (2003) served as an analysis framework which was applied to Peterborough's post‐disaster flood hazard mitigation program. Primary data were derived from semi‐structured key informant interviews (n=15) with senior local government officials, consultants, and community group representatives. Secondary data and direct observation were used to contextualize and extend research findings. The research revealed that post‐disaster flood hazard mitigation in Peterborough has featured strong citizen participation, and for that reason was relatively successful. However, there are at least three areas of post‐disaster planning where citizen participation could have been improved. The article concludes that the Brody, Godschalk, and Burby (2003) framework is a valuable guide for planning practice and an evaluative research tool that revealed a number of significant strengths and several weaknesses of the Peterborough flood hazard mitigation planning process.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.018
GPT teacher head0.317
Teacher spread0.299 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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