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Record W4409815206 · doi:10.1017/cri.2025.4

The Beehive’s Poison: Analysing Legal and Ethical Dilemmas Encircling Evidence Gathered from Sting Operations in India

2025· article· en· W4409815206 on OpenAlexaboutno aff
Laadli Singhania, Virendra Singh Thakur

Bibliographic record

VenueInternational Annals of Criminology · 2025
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsnot available
Fundersnot available
KeywordsStingBeehiveForensic engineeringEngineeringPolitical scienceEngineering ethicsBiologyEcology

Abstract

fetched live from OpenAlex

Abstract A potent investigative instrument in the fight against highly sophisticated criminal schemes camouflaged by layers of secrecy is the sting operation. However, its application provokes crucial questions of legality and admissibility. Additionally, lack of legal provisions governing sting operations in India has resulted in conflicting judicial stances, calling for clarity on this issue. Hence, this paper examines the intricate legal and ethical challenges surrounding sting operations, which, on one hand, aid in uncovering serious offences and foster public interest but, on the other hand, threaten to infringe privacy rights and fairness of trials. The paper analyses international practices in Canada and the United States of America, alongside judicial precedents and scholarly opinions in India, and recommends statutory inclusion of sting operations in the Indian legal system. The paper proposes stringent judicial control, elaborate ethical guidelines to avoid staging crimes, and regulations on media reporting to maintain the delicate balance of public interest versus personal rights. The paper concludes with a model draft for legislative reform that seeks to strengthen the idea of justice without weakening fundamental rights.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.915
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.183
GPT teacher head0.452
Teacher spread0.269 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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