Attribution of Liability among Multiple Tortfeasors under Negligence Law: Causation in Iran and England
Bibliographic record
Abstract
“Causation” possesses a considerable place in tort law of Iran and England particularly in the field of Negligence law. Existing differences in legal systems of Iran (as a Civil Law system) and England (as a Common Law system) make find a common perspective difficult to study causation but possible. This research focuses to compare causation in cases where more than one tortfeasors is involved in inflicting damage by negligence. This study also attempts to recognize differences and similarities between Iran and England in order to resolve ambiguities in Iran legal system through England legal system. The study was conducted in three sections including tortfeasors’ indenpendancy, tortfeasors’ contribution, and tortfeasors’ separate impact. This paper reports respectively: in case of tortfeasor independency, Iran law admits jointly and severally liability while England law offers a variety of approaches in various cases; in case of tortfeasors’ contribution, each tortfeasor is liable according to its effect on causing damage with few exceptions; and in case of tortfeasors’ separate impact, per tortfeasor is liable for inflicted damage which is only from oneself side. The results show England law can be considered to filling legal gap of Iran law regarding present identified differences and similarities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".