Corporate criminal liability and the identification principle: a critical and comparative analysis across Mauritius, US, UK and Canada
Bibliographic record
Abstract
Purpose The identification principle serves as a key tool in holding companies criminally accountable for acts of its agents, with the aim to secure convictions and promote a shift in corporate behaviour. Unfortunately, in Mauritius, the law is still not clear on how to engage the corporate criminal liability of the company although courts have attempted to apply the identification doctrine in some instances. Consequently, several corporate bodies are left unpunished for their criminal acts. Hence, the purpose of this paper is to evaluate the identification principle's applicability to corporate crimes in Mauritius. Design/methodology/approach To achieve the research objective, the black letter research method was adopted to collect secondary data by analysing the related laws on corporate criminal liability and a comparative analysis with some other countries’ rules on the subject matter was conducted. A desk-based approach and content analysis was used to collect this information. The countries selected for the comparison are the USA, UK and Canada. Findings From the critical analysis conducted in this paper, it is imperative for Mauritius to establish a more robust corporate criminal liability framework. The identified deficiencies, notably in Section 44(1)(a) of the Interpretation and General Clauses Act, should be reviewed and replaced with comprehensive norms with the goal of ensuring that corporate crimes are tackled properly. Such a proactive strategy not only empowers authorities to effectively address corporate crimes but also encourages corporate entities to take a proactive approach through the implementation of comprehensive compliance frameworks that are reviewed and updated on a regular basis. Originality/value At present, this study is among the few academic writings on corporate criminal liability in the context of Mauritius and it is being carried out with the aim of combining a large amount of empirical, theoretical and factual information that can be of use to various stakeholders and not only to academics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".