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Presentation of a Person for Identification under the 1960 and 2012 Criminal Procedure Codes: comparative Legal Analysis

2024· article· uk· W4416714412 on OpenAlexaboutno aff
Yevgen Yuryev

Bibliographic record

VenueHerald of criminal justice · 2024
Typearticle
Languageuk
FieldSocial Sciences
TopicWar, Law, and Justice
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal procedureSupreme courtLegislationVaguenessLegislatureIdentification (biology)Criminal investigationProcedural lawPopulation

Abstract

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The article presents an in-depth interdisciplinary analysis of the evolution of legal regulation and practical application of the identification lineup procedure in Ukraine, covering both codifications of criminal-procedure legislation – the 1960 Criminal Procedure Code of the Ukrainian SSR and the current 2012 Criminal Procedure Code of Ukraine. The study is scientifically and practically relevant for three main reasons. First, the state of war and large-scale population displacement have greatly complicated the use of live fillers, sharply increasing reliance on photo line-ups in investigative practice. Second, the lack of procedural status for “other persons” and the legislative vagueness of the criteria of similarity and “marked differences” create a high risk of discriminatory or manipulative conduct that undermines the admissibility of obtained evidence. Third, Supreme Court case-law on whether the “necessity” of a photo line-up must be justified is fragmented: some judgments demand detailed reasoning for choosing the simplified form, while others deem it admissible without such justification, which confuses pre-trial authorities and defense counsel. The methodology combines historical-legal, formal-dogmatic and comparative approaches with empirical methods. Sources include the 1958 Fundamentals of Criminal Procedure, the 1960 CPC of the Ukrainian SSR, the 2012 CPC of Ukraine, as well as related by-laws and draft departmental instructions. The judicial corpus comprises more than forty Supreme Court decisions and sixty judgments of local and appellate courts from 2018–2024, examined through content analysis and case study. The empirical component consists of a survey of thirty-one investigators, inquirers, and prosecutors from Kharkiv and the Kharkiv region, alongside contextual analysis of twenty-five criminal cases involving both live and photo line-ups. Descriptive and correlation statistics processed in R verified the most frequent procedural defects. Findings show that investigators choose photo identification in 90 % of practical cases; only 27 % of those procedures were supported by a properly reasoned order, and 43 % of the files contained signs of violating similarity requirements. Selecting fillers for live line-ups is impeded by refusals or an inability to find volunteers quickly, especially in frontline communities and during curfew. In more than 30 % of cases, the presence of handcuffs, lack of a belt or shoelaces, or other visible signs of detention led trial courts to declare the evidence inadmissible, although the Supreme Court did not always uphold that view. Based on the analysis, the authors propose modernizing Article 228(6) CPC by: Mandating a reasoned order from an investigator, inquirer, or prosecutor when choosing a photo line-up, with an exhaustive list of grounds such as urgency, security risks, lack of fillers, and wartime or emergency conditions. Setting minimum and maximum numbers of fillers – three to five – with a detailed list of impermissible differences. Granting fillers the procedural status of voluntary assistants to the investigator, with basic identity verification. Creating an electronic Register of Voluntary Line-up Participants containing verified photo and biometric data, integrated with an artificial-image generator for situations where recruiting real fillers is impossible or unethical. Additionally, a departmental algorithm for documenting circumstances that prevent a live line-up is proposed, ensuring transparency and safeguards for the defense. Implementing these changes would enhance evidentiary reliability, shorten the time needed to organize investigative action, reduce manipulation risks, and reinforce the principles of immediacy, adversarial balance, and proportionality in human-rights interference. Future research should explore psychological experiments on how filler number and homogeneity affect identification accuracy, comparative analyses of procedures in Denmark, the Netherlands, and Canada, and a cybersecurity audit of the proposed register and selection algorithms in the context of personal-data protection.

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)0.009
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 score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.008
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.092
GPT teacher head0.394
Teacher spread0.302 · 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
Published2024
Admission routes1
Has abstractyes

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