Accommodating ethnic and cultural factors in damages for personal injury
Bibliographic record
Abstract
In 1991, Fahmo Adan, her husband and three children came to Canada from Somalia and settled as refugees. Three months later, Ms. Adan approached Dr. Bernard Davis, an obstetrician and gynaecologist, regarding her fourth pregnancy. Six months later she gave birth to a healthy daughter. Ms. Adan then suffered recurring perineal pain. Dr. Davis eventually performed a tubal ligation resulting in permanent sterilization of Ms. Adan, which cured her of recurring pain. In 1992, Ms. Adan's three year old daughter died as the result of an accident, and soon after this event her husband left her. On a subsequent visit to Dr. Davis concerning recurring menstrual problems, Ms. Adan learnt of her earlier sterilization. Ms. Adan, who could neither read, write, nor speak English, had always been accompanied by an interpreter to her medical appointments. She commenced a negligence action against Dr. Davis on the grounds that he had failed to secure her informed consent to a procedure that lead to her permanent t Professor of Law, Faculty of Law, University of Windsor, and of the Faculty of Law, University of Auckland. A preliminary draft of this paper was first given at the Fourth Remedies Forum, Louis Brandeis Law School, University of Kentucky, (Louisville, Kentucky, November 2005) and at the Obligations Ill conference, T.C. Beime School of Law, University of Queensland (Brisbane, Queensland, July 2006). 1 wish to thank the assistance of Drew Sinclair and Michael Noonan, my research students, both of whom were generously funded by the Law Foundation of Ontario, and the comments of two anonymous referees.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".