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Record W4285386007 · doi:10.6000/1929-4409.2022.11.10

Ensuring Victims’ Participation in the Criminal Justice of Bangladesh

2022· article· en· W4285386007 on OpenAlexvenueno aff
Khandaker Farzana Rahman

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
FundersUniversity of Dhaka
KeywordsAdversarial systemCriminal justiceEconomic JusticeTheory of criminal justicePolitical scienceLawCriminologyFace (sociological concept)Sociology

Abstract

fetched live from OpenAlex

It is seen that if the main actors in a criminal justice framework are to be identified, the most commonly identified would be the alleged, his legal representative, the prosecutor and the judge. In our current legal system, the victim appears to be one of the overlooked and disregarded parties, when in reality they should be considered a vital stakeholder in the criminal justice process to secure justice. Due to adversarial legal system in Bangladesh the burden of proof lies upon the prosecution or victim in a criminal proceeding. There is hence no comprehensive law securing rights and participation of victims in criminal justice system though few supports exist for them. In accessing the justice system, victims face numerous challenges and the plight of crime victims continues to go from bad to worse. In this background, the research relies on qualitative methods to explore their status, participation and challenges in the justice system and lastly recommends how to make the justice system victim oriented.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.073
GPT teacher head0.385
Teacher spread0.312 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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