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Record W1518350120 · doi:10.1017/cbo9780511495007

Regional Private Laws and Codification in Europe

2003· book· en· W1518350120 on OpenAlexaboutno aff
Hector MacQueen, Santiago Espiau Espiau, Antoni Vaquer

Bibliographic record

VenueCambridge University Press eBooks · 2003
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionMember statesConflict of lawsPolitical scienceLawPrivate lawEuropean Union lawPhenomenonRelation (database)Comparative lawBusinessInternational trade

Abstract

fetched live from OpenAlex

Regions within European Union member states (such as Scotland in the UK and Catalonia in Spain) have their own legal systems: how will the process of 'Europeanization' affect them? This volume examines the phenomenon of 'regional' private law in the European Union, considering jurisdictions and laws below those of the member states and drawing comparisons with other such jurisdictions elsewhere in the world, such as Louisiana and Quebec. The whole is considered in relation to the development of European private law, and the use of codification in that process. This volume will be of interest to academic lawyers worldwide, advanced law students and European policy-makers.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.008
Scholarly communication0.0110.006
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.177
Teacher spread0.149 · 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 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

Citations9
Published2003
Admission routes1
Has abstractyes

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