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Record W2998893145 · doi:10.5430/ijhe.v9n2p70

A Comparative Study on the Legal Education Model in Saudi Arabia

2020· article· en· W2998893145 on OpenAlexvenueno aff
Awad Ali Alanzi

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)ApprenticeshipLegal educationLawPolitical scienceKingdomAdministration (probate law)Legal historyHistoryEngineering

Abstract

fetched live from OpenAlex

This research mootivates to identify the legal eduaction model in the Arab region and Saudi Arabia in particular. It is found that Arab legal system is derived from the Roman law and French models. The French teaching model is spread in the Kingdom through the professors educated from the French law schools. The french model of legal education follows the lecture approach. The formal legal education in the Kingdom is initiated by Institute of Public Administration (IPA) during 1970s. IPA offered two-years disploma after Sha’ria graduation. Afterwards, King Saud University started the systemetic legal education since 1980s. Law graduatea are required atleast 4-years law degree and 3-years apprenticeship in a law firm as a trainee lawyer.This research also differentiates the legal education system of France, Saudi Arabia and US. We find that that Saudi Arabian system is more similar to French system and lesser similar to the US system.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.115
GPT teacher head0.468
Teacher spread0.353 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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