May You Litigate in Interesting Times: Specific Performance, Mitigation, and Valuation Issues in a Rising (or Falling) Market
Bibliographic record
Abstract
This article provides practical insight and strategic guidance regarding how to properly structure the prosecution or defence of a claim in a rising and falling market, and what expert and fact evidence is necessary. First, the article discusses the threshold required to be awarded specific performance and how courts have interpreted Semelhago’s “uniqueness” test, especially in the context of property purchased for commercial investment purposes. Next, if specific performance is not awarded, the valuation date must be chosen. The authors propose a new “hybrid approach” for assessing damages whereby the loss based on actual cash follow up to the date of trial is measured (and a risk adjustment applied to reflect that revenues are never earned risk-free). The net present value of remaining cash flow is then calculated on the basis of the most recent data available at the date of trial. The proposed hybrid approach allows the plaintiff to receive the value of land less the cost to acquire it, plus in every claim month the plaintiff receives the cash it would have earned, but also assumes the risk of operating the land as of that time. Finally, in considering Southcott the authors address some strategic and practical considerations regarding mitigation and the needed evidentiary burden to consider.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".