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Record W3166507193

Disciplinary Responsibilities of Judges in Common Law Legal Systems: A Comparative Study in the Contexts of USA, England, and Canada

2020· article· en· W3166507193 on OpenAlexaboutno aff
Azadeh Abdollahzadeh Shahrbabaki

Bibliographic record

VenueJOURNAL OF LEGAL RESEARCH · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsLawDisciplineCriminal lawPolitical scienceCivil law (Civil law)Common lawPublic lawComparative lawPunishment (psychology)Private lawPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Disciplinary law, as a subcategory of public law, investigates the major regulations of the punishments for the employees of a highly creditable organization. Disciplinary law is different from criminal law and the purpose of punishment in these two types of law is not the same. In the criminal law, the purpose of punishment might be criminal deterrence, delinquent civilization and training, or victim cooling, whereas in disciplinary law, the major purpose of punishment is to monitor the maintenance and correct carrying of job responsibilities. Because of the position and authority of judges, their behavior would have important positive or negative effects on the lives and rights of litigators. Therefore, in the developed countries, the behavioral manuals and moral codes are set for the definition of disciplinary responsibilities of judges. In the same vein, the control of judge behavior law was legislated in 1390 in Iran. A comparative study of the different legal systems can contribute to the identification of weaknesses and strength points of the legislated law. Moreover, the investigation of position and disciplinary regulations of judges in those countries which have their legal system based on the Common Law can be a suitable criterion of measurement for identification of predicted shortcomings in the law and regulations of Iran with respect to the position and role of judges in these systems. For this purpose, the present study investigates the issue of judge’s disciplinary responsibilities in the contexts of United States of America, England, and Canada.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.377

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0130.006
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
GPT teacher head0.512
Teacher spread0.257 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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