The Nexus between Criminology and the Corporate Sector: A Critical Overview
Bibliographic record
Abstract
The intricate interaction between criminology, company operations, and the regional and historical differences in criminal laws is examined in this study using a qualitative research methodology. This study compares how the criminal justice system handles corporate malfeasance to how it handles crimes committed by individuals in order to investigate the effectiveness and challenges of applying criminal law to enterprises. The majority of the data collected comes from secondary sources. The results show that managing corporate misconduct is different from managing individual transgressions, which creates challenges for enforcement and punishment. The results of the study show that the criminalisation of particular behaviors is significantly influenced by legal frameworks and social norms. The researchers came to the conclusion that improving corporate governance, strengthening enforcement protocols, passing laws protecting whistleblowers, and launching community education-based public awareness campaigns could all potentially increase the effectiveness of the criminal justice system in combating corporate crime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".