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Record W7055537665

Colonial Lag and Surinamese Legal Dutch

2017· dissertation· en· W7055537665 on OpenAlexaboutno aff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2017
Typedissertation
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLegal researchVariety (cybernetics)Legal pluralismLegal realismInternational Legal English CertificateLegal opinionEmpirical legal studiesLegal professionLegal formalism
DOInot available

Abstract

fetched live from OpenAlex

Legal language, the language of law, has its own characteristics which are not only expressed in different languages of different legal systems but also in different languages in the same legal system (e.g. French and English in the Canada). In the Netherlands and Suriname, the official language and the legal language is Dutch. However, there are differences in the Dutch used in these countries. The legal systems of the Netherlands and Suriname have gone their own way since 1975. This is expected to show in the Legal Dutch of both countries. This research investigates how the Legal Dutch of Suriname and the Netherlands, respectively, have developed. The aim of this study is to compare the legal language of these two legal systems and detect differences in the usage of Dutch legal language. As claimed by Marckwardt in 1958, (post)colonial varieties of a language change less than the variety spoken in the (former) mother country. This theory is referred to as the ‘colonial lag’. This research will be limited to criminal law. Within this area of law, legal texts will be compared. The expected outcome is that Surinamese Legal Dutch will be more conservative than the Legal Dutch in the Netherlands.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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