Sometimes It's a Big Ask, but Sometimes It's a Big Outcome: Community Participation in Flood Mitigation
Bibliographic record
Abstract
There are often calls for more community or citizen involvement in planning of all types, including hazard mitigation (Brody 2003, Burby 2001, Gregory 2000, Pisaniello 2002, Tarrant 1997/1998). There are statutory requirements for community involvement in risk management planning in a number of countries, including Australia (Burby 2001, Department of Justice Canada 2000, Handmer and Parker 1992, Kennedy 1991, New South Wales Government 2004, Walker 1979, Zamecka and Buchanan 2000). Reasons for supporting community involvement include informing and educating the community about issues, tapping into community knowledge and possible solutions, understanding community preferences for hazard management equity, and achieving practical and effective outcomes (Godschalk et al 2003). However, there has been considerable debate on the efficacy of citizen involvement in hazard mitigation planning (Burby 2001) particularly from the view of planners and agencies actively seeking citizen involvement.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".