Corporate Responses to Tackling Modern Slavery: A Comparative Analysis of Australia, France and the United Kingdom
Bibliographic record
Abstract
Abstract The business and human rights agenda is gaining momentum internationally, perhaps best evidenced through recent legislative responses to tackling modern slavery. Using a reflexive law lens, we analyse three recent laws – the UK Modern Slavery Act 2015, the French ‘duty of vigilance’ law of 2017, and the Australian Modern Slavery Act 2018 (Cth). The three laws, or their accompanying guidance, share characteristics in terms of reporting requirements: the supply chain; risk mapping/assessment and management; analysis of subsidiary and supply chain risk; and effectiveness. The French Act has a broader scope as it is a due diligence, rather than a reporting law and includes obligations with regard to human rights and fundamental freedoms, health and safety, and the environment. It is the only Act of the three with substantive penalty provisions. All reporting requirements in the French and Australian Acts are mandatory, but the UK Act has limited mandatory reporting requirements. We find that only 22 companies globally will be required to report under all three laws. Using a subset of this dataset, we analysed 59 French vigilance plans and UK modern slavery statements published by nine manufacturing companies. This provided some preliminary analysis of how businesses have reported under the French Droit de Vigilance and the UK Modern Slavery Act (reports under the Australian Modern Slavery Act for these companies were not published at time of writing). Overall, businesses are using less demanding measures such as introducing policies and delivering training more commonly than the somewhat more resource-intensive activities such as audits. The more onerous requirements of the French law were reflected in the content and level of detail in the vigilance plans, compared with the UK modern slavery statements. However, for some companies, there were strong similarities between the UK and French publications, indicating ‘creep’ from the French Act into UK reports or a ‘race to the top’.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".