An Empirical Analysis of the Nuclear Liability Act (1970) in Canada
Bibliographic record
Abstract
The Nuclear Liability Act (1970) limits the liability of nuclear reactor operators in Canada to the first $75m of any off-site damage done by an accident. In recent litigation, the limitation has been challenged. It has been argued that the implicit subsidy which such a provision confers encourages the use of nuclear over other fuel sources and reduces safety incentives. During the litigation, it was contended that the value of the subsidy could be as high as 12 to 16 cents per kWh. We use numerical curve-fitting techniques to evaluate the subsidy using data implicit in insurance premiums and under a range of expert assessments regarding worst scenarios. In most cases, the subsidy is found to be less than 1 cent per kWh, and in no case is it greater than 4 cents. While the uninternalised costs are not trivial they are smaller than existing estimates of those associated with the use of alternative fuels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".