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Record W2887570016 · doi:10.1177/1365712718787523

Social science and humanities evidence in <i>Charter</i> litigation

2018· article· en· W2887570016 on OpenAlexafffundabout
Jocelyn Downie

Bibliographic record

VenueThe International Journal of Evidence & Proof · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsDalhousie University
FundersPierre Elliott Trudeau Foundation
KeywordsCharterSociologyHumanitiesPolitical scienceDigital humanitiesLawArt

Abstract

fetched live from OpenAlex

Carter v Canada (Attorney General) is a Canadian case that famously struck down the Canadian Criminal Code prohibitions on euthanasia and assisted suicide (now known collectively as medical assistance in dying or MAiD). The most significant issue in the Carter case was that of the status of MAiD. However, this case is also interesting to explore in relation to the issue of the use of expert evidence from social science and humanities researchers. In this paper, I offer reflections as an academic trained in philosophy and law but not expert in the use of social science and humanities evidence in litigation. As someone who was inside the litigation but outside the generation of the evidence, I seek to bring a perspective that may be useful to practitioners who might be thinking about working with academics and academics who might be thinking about getting involved in constitutional litigation that relates to their field of study.

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)0.009
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.347
GPT teacher head0.523
Teacher spread0.176 · 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.

Study designObservational
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

Citations2
Published2018
Admission routes3
Has abstractyes

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