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Record W2792052333

Accommodating ethnic and cultural factors in damages for personal injury

2007· article· en· W2792052333 on OpenAlexaboutno aff
Jeff Berryman

Bibliographic record

VenueResearchSpace (University of Auckland) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupDamagesPersonal injuryPolitical scienceSociologyAnthropologyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.004
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.133
GPT teacher head0.452
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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