Downstream Human Rights Due Diligence: Informing Debate Through Insights from Business Practice
Bibliographic record
Abstract
Abstract The United Nations Guiding Principles on Business and Human Rights conceive of human rights due diligence (HRDD) as covering potential impacts across value chains, including downstream. The proposed EU Corporate Sustainability Due Diligence Directive and the revision process of the OECD Guidelines for Multinational Enterprises have sparked renewed discussion on how and whether companies should conduct HRDD downstream to identify and prevent or mitigate adverse human rights impacts. Whilst some debate has occurred previously on downstream HRDD, this has predominantly centred on specific sectors, products and services where the links to egregious human rights harms may be more readily identifiable. This piece seeks to inform the current debate by broadening the examples of sectors, products and services and current business practice which demonstrate the critical need for, and ability of, companies to consider human rights risks downstream.
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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 | 0.060 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.120 |
| Scholarly communication | 0.036 | 0.040 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".