IMPACT OF THE 1998 ICE STORM ON THE EASTERN ONTARIO MAPLE SYRUP INDUSTRY: A CASE STUDY OF NATURAL DISASTER POLICY IN CANADA
Bibliographic record
Abstract
Under Canada's Disaster Financial Assistance Arrangements (DFAA), the federal government can provide provinces with funds for emergency response and recovery in the event of a natural disaster. This assistance has historically been provided on an ad hoc basis. In recent years, the amount of DFAA assistance has significantly increased without any auditing to determine how effective and efficient these expenditures are in offsetting economic losses due to natural disasters. The goal of this paper is to examine the implications of natural disaster compensation and assistance programs for economic efficiency. A framework is developed to determine if government assistance expenditures have offset economic losses to a specific industry using a case study of the 1998 ice storm and the eastern Ontario maple syrup industry. Projections of damage recovery are used to measure the economic impact of the storm, and a comparison is then drawn between the change in producers' welfare and government assistance. The implications of the findings for the case study and for future natural disaster assistance programs in Canada are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".