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Record W2995659873 · doi:10.7202/1065066ar

IMAGES OF LEGAL HUMANISM

2019· article· en· W2995659873 on OpenAlexvenueno aff
Douglas J. Osler

Bibliographic record

VenueSurfaces · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsHumanismPhilologyInterpretation (philosophy)JurisprudenceLawClassicsLegal historyHistoryPhilosophyPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

Legal humanism is an expression generally used to refer to the study of Roman law by 16th Century philologists. Coming a century an a half after what is generally taken to be the birth of humanism, it is generally understood to be part of humanism mostly in its interest for the historical interpretation of Latin texts rather than for participating in the value orientation of humanism. Strangely enough, legal history has taken the philological work of legal humanism for granted not seeing that legal humanism was itself inscribed in history. First, we should note that when it was being developed in the 16th Century, this philological work in Roman law was marginal if one compares publications in that field to publications of medieval Italian jurisprudence. Secondly, legal history has tended to diminish the presence of interpretation in the work of legal humanists who probably more than is generally acknowledged adapted to their times the Latin texts inventing values which have nothing to do with the Roman sources. Legal humanism has never been used to mean the legal aspects of humanitarianism and probably needn’t to be. If the work of interpretation of Roman law has opened some windows into humanistic values, Roman law itself was the codification of an unjust power structure.

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)0.002
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.046
Scholarly communication0.0100.007
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.012
GPT teacher head0.283
Teacher spread0.271 · 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
GenreOther

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

Citations4
Published2019
Admission routes1
Has abstractyes

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