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Record W2916002909 · doi:10.5539/jpl.v12n1p74

The Accused Privacy Rights in the Sudanese Legal System

2019· article· en· W2916002909 on OpenAlexvenueno aff
Adam Mohamed Ahmed Abdelhameed, Kamal Halili Hassan, Parviz Bagheri

Bibliographic record

VenueJournal of Politics and Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsLawConstitutionPolitical scienceRight to privacyHuman rightsThe Right to PrivacyLegislationStatutory lawCriminal procedureLaw enforcementBill of rightsCriminal codeCriminal law

Abstract

fetched live from OpenAlex

The purpose of this article is to discuss the rights of the accused person in the Sudanese legal system. Similar with other criminal justice systems, the Sudanese law do provide rights for the accused person to enable him or her to defend him or herself. These rights are considered as the core idea behind the thinking of human rights in the criminal proceedings. However the problematic issue here is to what extent does Sudanese law provide and protect the accused right especially the privacy right. It is also pertinent to balance the law enforcement interest in evidence collection in criminal proceedings with the privacy right of the accused in the Sudanese legal system. We found that there are evidence of privacy right protection on the accused within the Sudanese legal system. The result of this research shows that in Sudan, the privacy right was provided for the first time at the constitutional level in the T1973 Constitution (Articles 42 and 43). It has later received recognition in the 1985 Transitional Constitution (Articles 24 and 30), the 1998 Constitution (Article 29) and the 2005 Interim National Constitution (Article 37). At the statutory level, legislative protection is given to this right in the Penal Code 1991 (Section 166), the Code of Criminal Procedure 1991 (Sections 86 through 95) and the Informatic Offences (Combating) Act 2007 (Sections 16 and 6). The method adopted in this article is a qualitative content legal analysis of primary and secondary data obtained from legislation, case-law and various literature.

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)0.005
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.012
Scholarly communication0.0070.005
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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