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Record W7112719198

Поняття та класифікація електронних доказів у кримінальному процесі у країнах світу

2025· article· uk· W7112719198 on OpenAlexaboutno aff

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2025
Typearticle
Languageuk
FieldSocial Sciences
TopicEducation, Law, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsConfidentialityContext (archaeology)Digital evidenceKey (lock)Electronic dataAuthentication (law)The InternetDigital forensicsData Protection Act 1998
DOInot available

Abstract

fetched live from OpenAlex

In the context of digital transformation, electronic evidence plays an extraordinarily important role in criminal proceedings. The development of information technologies and the widespread use of digital communications have significantly altered the nature of evidence that can be used in legal proceedings. Electronic evidence encompasses a wide range of digital information, including emails, mobile device data, computer files, video recordings, surveillance camera footage, social media data, and other forms of electronic information. These pieces of evidence are crucial for establishing facts of a crime, as well as identifying the subject, circumstances, and consequences of a criminal offense. This article provides a comprehensive analysis of the concept of electronic evidence, its classification, and the peculiarities of legal regulation in various countries around the world. Special attention is given to countries such as the United States, the United Kingdom, Canada, Australia, Germany, and Japan. A comparative analysis of legal norms regulating the use of electronic evidence in these countries is conducted, allowing for an examination of different approaches to the collection, preservation, authentication, and use of digital evidence. Each of the countries analyzed has its own specific requirements and procedures related to electronic evidence, but a common trend is observed—the need to ensure the reliability and authenticity of data. The study addresses key issues related to the use of electronic evidence, such as ensuring its procedural admissibility, protecting human rights during the collection and use of data, and addressing issues of confidentiality and data security. Specifically, the article analyzes the authentication of electronic evidence, as well as its compliance with procedural requirements established by national and international norms. The article also provides a detailed examination of legislative approaches to electronic evidence in different jurisdictions. In the United States, the Federal Rules of Evidence play a key role in defining procedures for establishing the credibility of digital data, while in the United Kingdom, the Criminal Justice Act 2003 regulates the admissibility of electronic evidence in court. Canadian legislation, including PIPEDA and the Criminal Code of Canada, establishes strict requirements for the collection and processing of electronic evidence. In Australia, important provisions are contained in the Electronic Transactions Act 1999, which regulates the use of electronic documents in legal proceedings. In Germany, the Strafprozessordnung (Code of Criminal Procedure) provides rules for electronic evidence, while in Japan, laws related to confidentiality and information security in the digital environment play a significant role. The article also highlights issues related to international cooperation and the harmonization of legal norms between countries, particularly in terms of exchanging electronic evidence and recognizing its validity within international legal frameworks. The balance between ensuring the security and confidentiality of information and the rights of individuals affected by such evidence is also discussed. Recommendations are made for improving the legal framework, adapting national legislation to the challenges of the digital age, enhancing the effectiveness of international cooperation in this field, andcreating universal standards for the collection and analysis of electronic evidence.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.032
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.005
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0320.016

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 head0.077
GPT teacher head0.397
Teacher spread0.320 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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