Challenges in and Opportunities for International Collaboration: Costing Flood Damages and Losses across Canada, Mexico, and the United States
Bibliographic record
Abstract
Abstract Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye toward addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and subnational jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in the future. We discuss three major research barriers: gaps in knowledge and research capacity, differences in data collation and analysis methods across the three countries, and linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".