Citizen Participation in Post‐disaster Flood Hazard Mitigation Planning in Peterborough, Ontario, Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".