Planning for climate change in a flood‐prone community: municipal barriers to policy action and the use of visualizations as decision‐support tools
Bibliographic record
Abstract
Abstract Efforts are intensifying to design effective flood management strategies that account for a changing climate and that make use of the wealth of resources and latent capacities associated with action at the local level. Municipalities, however, are subject to a host of challenges and barriers to action, revealing the critical need for sophisticated participatory processes in support of municipal decision‐making under conditions of considerable uncertainty. This paper examines a new process for envisioning local climate change futures, which uses an iterative, collaborative, multistakeholder approach to produce computer‐generated 3‐dimensional images of climate change futures in the flood‐prone municipality of Delta, British Columbia, Canada. The process appeared to forge communicative partnerships, which may improve the legitimacy and effectiveness of the flood management and climate change response discourse in the municipality of Delta, and may lead to locally specific and integrated flood management and climate change response strategies. We concluded that, while an enabling context and normative pressures are clearly integral to effective action, so too is the type and mode of presentation of information about climate futures.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".