Bibliographic record
Abstract
When I began this article my main objective was to show why the concept of mitigation of damage, which is so extensively used in common law, was apparently non-existent in civil law. Right from the beginning, however, I found conclusive evidence which proved that the concept of mitigation actually exists in civil law too; my purpose was then transformed into explaining how this concept works in two systems of law that are so different in their approaches and their methodologies. In order to make this study manageable, I have focused on the links between the concept of mitigation and the problem of pecuniary loss following a breach of contract. Consequently, issues pertaining to tort, physical injuries to persons and things, and claims to liquidate sums, as in debt, will be dealt with only incidentally. Regrettably, this course of action will leave open many interesting questions related to mitigation, mainly in tort but also in contract. Nevertheless, I trust that the present study will constitute a useful basis for further analysis on this subject. I have divided this work into two parts, devoted to the two phases of recovery following a breach of contract. The first phase concerns the choice of which losses fall under the protection of the law, among all those claimed by the plaintiff. I propose to call this phase measuring the extent of the loss. The second phase involves the determination of what the defendant will have to do in order to compensate the plaintiff; when this compensation takes a pecuniary form it involves the assessment of the pecuniary value of the loss. The first of these phases primarily concerns the extent of losses and the question of what damage counts for compensation; this particular aspect of the issue of mitigation is the subject of Part I of this article. The connection between mitigation and the pecuniary evaluation of a plaintiff's damages is examined in Part II where I focus on the effects of inflation and other factors that influence the cost of compensation. Finally, from a comparative point of view, one of the main interests of the present study lies in observing that the concept of mitigation has achieved a different status in civil law and in common law. The conclusion of this work explores this situation, and aims at explaining the historical and juridical circumstances that may have caused common law to attain higher levels of generality and of abstraction than civil law with regard to the issue of mitigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".