Mandatory human rights and environmental due diligence in practice: key insights from France and Germany
Bibliographic record
Abstract
On 25 July 2024, the Directive on corporate sustainability due diligence (Directive 2024/1760) entered into force. According to the European Commission, Member States have to transpose the Corporate Sustainability Due Diligence Directive (hereinafter «CSDDD» or «Directive») into national law and communicate the relevant texts to the Commission by 26 July 2026. Meanwhile, member states of the UN have been gathering for the 10th time to discuss a potential international treaty on BHR that would, essentially, oblige the states that sign to implement mandatory human rights and environmental due diligence (hereinafter «mHREDD») laws. Potential legislative initiatives on mHREDD are being discussed worldwide, including in Canada, Brazil, Colombia, South Korea, Tunisia and Mexico. This makes it an ideal time to evaluate the experiences of countries that have already implemented mHREDD laws. As BHR practitioners from France and Germany, our analysis highlights the experiences in these two countries: France, the global pioneer with its Duty of Vigilance Law (LdV) enacted in 2017, and Germany with the more recent implementation of its Supply Chain Due Diligence Act in 2021 (hereinafter «LkSG»). We publish a first summary of the most important aspects from our view, based on a larger article we published recently, that will be updated and complemented with new insights.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".