Making Lease Payments a Lessor Problem
Bibliographic record
Abstract
The frustration of purpose doctrine is a contracts defense that has garnered increased interest since the COVID-19 pandemic’s initial wave. To manage this public health emergency, many governments have issued orders restricting the operation of businesses. These orders, while necessary, put commercial lessees in a bind once it came time to pay rent because these restrictions drastically cut their profits. Other frustrating events, like war and natural disasters, cause the same problems, yet the current frustration of purpose doctrine is too narrow to be practically helpful to these lessees. This Note examines the English and Canadian frustration doctrines and draws on both in proposing two alterations to the American doctrine. These alterations would remedy the doctrine’s ineffectiveness, brought to light recently by the COVID-19 pandemic, and would attempt to ensure that the risk now falls on the party better equipped to bear it—the lessor.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".