Competing on Climate Change: An interprovincial, longitudinal review of emerging environmental risks to Canadian homeowners
Bibliographic record
Abstract
In an era of accelerated climate change, Canadian homeowners face growing financial exposures to environmental risks, and climate-related property damage now represents the largest aggregate cause of losses in the global insurance industry (Mills, 2012, p. 1424). This study presents data regarding hydrological, meteorological, and wildfire disasters occurring in Canadian provinces from 1970 to 2010. The rising incidence of natural disasters suggests that natural disasters are affecting an increasing number of Canadians across all provinces. In light of this data, the researcher recommends that Canadian insurers implement a “4-C” strategy to help reduce the human impact of future natural disasters: (1) Coaching local communities to adapt to climate change; (2) Consensus-building around common consumer risks; (3) Collaborating with governments to protect against catastrophic losses; and (4) Cooperating with consumers to co-insure frequent events. Finally, it is recommended that risk capital be invested carefully and sustainably, so that the 4-Cs is customized to address emerging challenges specific to each climate zone.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".