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Record W2297248134 · doi:10.1093/ijl/ecv036

Máirtín Mac Aodha (ed.). 2014.<i>Legal Lexicography. A Comparative Perspective.</i>

2015· article· uk· W2297248134 on OpenAlexaboutno aff
Weronika Szemińska

Bibliographic record

VenueInternational Journal of Lexicography · 2015
Typearticle
Languageuk
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLexicographyTerminologyLinguisticsPerspective (graphical)Order (exchange)Civil law (Civil law)SociologyHistoryLawPolitical sciencePhilosophyCommercial lawComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Legal Lexicography. A Comparative Perspective is one of few books devoted to this, as the blurb maintains, ‘most neglected aspect of the discipline of jurilinguistics’ and an attempt to fill the gap in modern lexicographic research. It presents contributions by experts in comparative law, legal history, terminology, terminography, translation and linguistics. The volume is directed at both scholars and practitioners, representing various perspectives: anachronic and diachronic, mono- and bilingual, and from common and civil law traditions in order to reflect current academic thinking (p. 3). The book consists of fifteen chapters by various authors, usually with either legal or linguistic background, who represent ten jurisdictions. Three chapters are written in French and include English language introductions which summarise the contents. The volume also contains a list of figures, a list of tables, acknowledgements, notes on contributors, a foreword by Lionel Smith from the Faculty of Law at McGill University, and an introduction by the editor of the volume, Máirtín Mac Aodha, a lawyer-linguist at the Council of the European Union and a researcher at the Université de Strasbourg. Two indices — for English and for French chapters, respectively, close the volume. Each chapter has its own bibliography.

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)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.402
Teacher spread0.330 · 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
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

Citations1
Published2015
Admission routes1
Has abstractyes

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