Car Crashes, Personal Injury Litigation, and Frivolous Defenses in Alberta and Colorado
Bibliographic record
Abstract
This Article is a comparative empirical study of car crash litigation in Alberta, Canada and Colorado, USA. The first part of the Article compares the rates of car crash injuries and litigation between Alberta and Colorado. The Article assembles data for what sociolegal scholars typically call the dispute pyramid, but I argued that a salmon run is a better metaphor for the winnowing of injuries through lumping, claiming, settling, and litigating. The Article shows that Albertans have a safer driving culture than do Coloradans. Surprisingly, the data also show that Albertans file car crash lawsuits much more frequently than Coloradans, notwithstanding the American reputation for litigiousness. I suggest that something within the settlement of property damage only cases likely accounts for the difference. The second part of the article presents a summary of empirical data and argument concerning the pleading of frivolous defenses by American insurance defense lawyers. I compare the pleading of these defenses to the Alberta Rules of Court. I am especially interested in hearing from Alberta and Canadian lawyers regarding differences between the Canadian tort system and that of 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".