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Record W4200363401 · doi:10.1017/9781108894760.002

How to Do Comparative Law: Some Lessons to Be Learned

2021· book-chapter· en· W4200363401 on OpenAlexaff
Mauro Bussani

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsMcGill University
Fundersnot available
KeywordsConciliationArgument (complex analysis)DreamDiversity (politics)LawSociologyMagnum opusPolitical scienceArtPsychologyMediationLiterature

Abstract

fetched live from OpenAlex

This Chapter appraises the range and depth of Patrick Glenn's scholarly legacy by exploring some of the many lessons that can be drawn from his opus magnum Legal Traditions of the World. To this purpose, the chapter will focus on three key notions underlying Glenn's chefs-d'œuvre: Law, Tradition, and Conciliation. The argument is that Glenn's findings have in multiple ways enlightened the understanding of what the law is (outside and also inside the West), as well as the relentless dynamics within and between legal traditions. Through these findings, Glenn has also provided us – his friends, colleagues, readers – with a powerful intellectual tool to pursue his conciliatory dream towards a world of tolerance and diversity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.097
GPT teacher head0.296
Teacher spread0.199 · 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 designNot applicable
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

Citations1
Published2021
Admission routes1
Has abstractyes

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