Bibliographic record
Abstract
Globalization has come to financial markets and to innumerable industries. U.S. businesses export and import goods and products; many have done so for decades. Domestic companies that sell materials online almost certainly do some international business. American corporations have foreign facilities or operations. Americans travel internationally with relative ease. For those living in states that adjoin Canada or Mexico, international travel can be accomplished simply by driving across the border. At the same time, insurance policies sold in the United States frequently contain territorial limitations on coverage that superficially seem out of place when compared to many aspects of modern business and to individual habits or practices. The concept of globalization is in many instances missing from the pages of insurance policies, or is at the very least significantly circumscribed. For example, with narrow exceptions, a standard liability insurance policy provides coverage only if the underlying occurrence takes place in the United States (including its territories and possessions), Puerto Rico, or Canada, or in transit between their ports. Insurers territorially limit their coverages for rating purposes. Courts generally enforce geographic restrictions in insurance policies. But they do not always do so, and territorial limitations may in any event be a source of unpleasant surprises for insureds who, for one reason or another, thought they had coverage for an occurrence only to learn that they were uninsured by virtue of an accident’s location. Territorial limitations also pose analytical challenges for courts and lawyers in cases that involve allegedly tortious conduct that occurred both within and outside the specified coverage territory. Finally, and perhaps surprisingly, coverage territory clauses may frustrate insurers by potentially subjecting them to personal jurisdiction in remote or unexpected forums. In short, territorial limitations on insurance coverage represent an important but frequently overlooked or underappreciated aspect of insurance law. These limitations potentially test everyone concerned in the event of a loss. In an effort to shed some light on this important aspect of insurance law, this Article examines the key issues raised by territorial limitations in a fashion useful to courts, lawyers, and scholars alike.
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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 | 0.006 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".