Implications of disclosure and non-disclosure of flood hazard maps – a synthesis for the Canadian context
Bibliographic record
Abstract
Flood is an increasingly costly and impactful hazard in Canada. Risk management approaches need to be applied to stem rising costs and impacts of floods. The foundational tool that supports many risk management strategies is the development of flood mapping products. In Canada, however, there is only a patchwork of flood mapping available, and there is further variability in the accessibility of this information to private and public sectors. This article draws on published studies to synthesize the potential benefits and disbenefits of making flood maps more available and accessible in the Canadian context, with a focus on real-estate transactions, but also with consideration of implications to land use planning, flood insurance uptake, and social equity impacts. The review highlights that accessibility and regulated disclosure of flood maps reduce property values marginally, but not to the full discount that should be applied if flood risks were fully accounted for or realized. There are also substantial benefits of making flood mapping products more accessible, including greater social equity, by removing the challenge of data asymmetry (where some buyers and sellers have better information than others), better emergency preparedness, and increased insurance uptake to manage residual risks.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".