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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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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