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Record W1872345973

Choice of Law for Unjust Enrichment: Rome II and the Common Law

2008· article· en· W1872345973 on OpenAlexaff
Stephen G. A. Pitel

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsWestern University
Fundersnot available
KeywordsLawStatutory lawCommon lawChoice of lawCivil law (Civil law)Unjust enrichmentComparative lawPolitical scienceScots lawPublic lawMunicipal lawRule of lawPrivate lawSources of lawConflict of lawsRestitution
DOInot available

Abstract

fetched live from OpenAlex

From 11 January 2009 the common law choice of law rule for unjust enrichment will be replaced in Member States of the European Union by the provisions in the Rome II Regulation. The first part of this article will analyze the new rule in Article 10, setting out its provisions and explaining how they will operate. For civil lawyers, that analysis will be reasonably familiar. But for lawyers in Europe’s common law legal systems — the United Kingdom and Ireland — it will be quite different from what preceded it. Until now, in those countries there was no statutory or codified choice of law rule for unjust enrichment. Rather, the rule was left to the case law, and was what judges in the few cases that had been decided had stated the rule to be. For the United Kingdom and Ireland, the adoption of Rome II means that the choice of law rule for unjust enrichment can be much more easily and confidently stated. A key question, however, for the common law countries is whether the content of the new rule is consistent with, or an improvement on, the earlier common law rule. The second section of this article will explain the common law rule and compare it with the new rule in Article 10. This comparison will not only highlight how the law in the United Kingdom and Ireland has been changed, but will also provide guidance as to how the courts in those countries might apply Article 10.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.312
Teacher spread0.287 · 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 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

Citations2
Published2008
Admission routes1
Has abstractyes

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