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Record W3186477871

The Phenomenology of Medico-Legal Causation

2017· article· en· W3186477871 on OpenAlexaff
Nicholas Hooper

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCounterfactual thinkingCausationDoctrineEpistemologyPhenomenology (philosophy)AdjudicationLegal doctrineNarrativeCausality (physics)Context (archaeology)ElitePragmatismLaw and economicsLawPsychologySociologyPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The language of counterfactual causation employed from the bench obscures the analytical vacuity of the "but for" test This paper takes issue with the consistent recourse to "common sense" as a methodological tool for determining the deeply complex issue of causality Despite manifestly empty gestures to eg robust pragmatism the current approach imposes the dominant values of the judiciary in a manner that perpetuates the current distribution of power Whatever the merits of counterfactual inquiry its legal iteration requires judges to construct a hypothetical narrative about "how things generally happen" This in turn impels a uniquely comprehensive brand of judicial creativity The results are productively examined in the context of medical malpractice where the phenomenological lens foregrounds the connection between meaningless doctrine and the protection of the medical elite

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)0.010
metaresearch head score (Gemma)0.020
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.082
Scholarly communication0.0090.017
Open science0.0020.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.401
Teacher spread0.349 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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