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Record W4389333131 · doi:10.7202/1105883ar

COMPARATIVE LAW IN DEVELOPING COURT PRACTICE IN SMALL JURISDICTIONS – MISSION POSSIBLE

2013· article· en· W4389333131 on OpenAlexvenueno aff
Irene Kull

Bibliographic record

VenueRevue de droit Université de Sherbrooke · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Law Studies
Canadian institutionsnot available
FundersEesti Teadusfondi
KeywordsLawComparative lawLegal researchPolitical sciencePrivate lawEmpirical legal studiesCivil law (Civil law)Framing (construction)Public lawEngineering

Abstract

fetched live from OpenAlex

: The purpose of this article is to discuss the transformative role of comparative law in the development of small nations with short legal histories, compelled to rely on borrowed ideas, concepts and regulations to create their own legal culture and reality. As a small country Estonia does not possess the necessary legal expertise or court practice in many specific areas. Here, comparative law in both of its functions – rules-based and context-based research – provides great help. The article explains how comparative law has been used in the framing of the Estonian system of civil law as well as of specific statutes. In the drafting process, a comparative law method was used to provide drafters with strategic options drawn from an array of legal systems, codes and provisions. The notion of legal transplants is widely used and explained, but in small countries, using transplants may be the only technique for building up a national legal system. The article also focuses on analysing whether the courts may use comparative law as a legal basis for developing private law. The last part of the article is dedicated to the use of comparative law in Estonian court practice, in order to illustrate that there are limits to using comparative law methods. Models of core countries may differ in their suitability because of the differences in ability to successfully absorb legal transplants or because the level of development of the society as a whole does not support the reception of transplants.

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)0.036
metaresearch head score (Gemma)0.034
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: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0110.040
Scholarly communication0.0150.016
Open science0.0020.010
Research integrity0.0030.003
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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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
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
Published2013
Admission routes1
Has abstractyes

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