Reforming Canada’s Disaster\nAssistance Programs
Bibliographic record
Abstract
Le programme canadien d’aide financière en cas de catastrophe (AFCC) prévoit le versement, par l’intermédiaire des provinces, de prestations subventionnées par les accords fédéraux d’AFCC (AAFCC), au-delà de seuils de dommages qui ont été triplés en 2015. L’incidence et la gravité des catastrophes augmentent. Les inondations sont les plus onéreuses, suivies des tempêtes et des feux incontrôlés. L’auteur analyse les changements de politiques qui s’imposent et se penche plus particulièrement sur les inondations. Selon lui, l’assurance habitation devrait intégrer la protection contre les inondations qu’il conviendrait de subventionner dans les régions présentant un risque élevé, à taux régressifs au fil du temps, de manière à encourager l’autoprotection ou le déplacement. Des rachats devraient être offerts et imposés dans les régions où les risques sont le plus élevés. D’autres réductions de l’aide octroyée par le truchement du programme devraient être envisagées, afin que soient renforcées les motivations à atténuer tous les types de catastrophes. Abstract: Canada’s disaster financial assistance (DFA) system provides benefits through the provinces, subsidized by the federal DFA Arrangements (DFAA) above damage thresholds that were tripled in 2015. Disaster incidence and severity is increasing. Flooding is most costly, then storms and wildfire. The need for policy changes is analyzed, with particular attention to flooding. It is argued that flood coverage should be required under home insurance, subsidized in high-risk areas at rates declining over time to encourage self-protection or relocation. Buyouts should be offered and made mandatory in the highest risk areas. Further DFAA assistance reductions should be considered, to give stronger incentives for mitigation of all disasters.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".