Navigating the security/human rights nexus in the transition to green energy:Mandatory due diligence amid changing political realities in the Netherlands
Bibliographic record
Abstract
The Netherlands has committed itself to accelerating the transition to green energy. For the enormous amount of critical raw minerals (CRM) needed for this transition, the country is fully dependent on imports from a relatively small number of countries, mostly in the Global South. This article discusses how the Netherlands navigates between the enhanced demand for CRM and the expectation that sustainability requirements are not compromised. It examines to what extent the foreseeable legal infrastructure in the Netherlands and the European Union (EU) provides sufficient safeguards that CRM sourcing respects human rights and the environment, and meaningful engagement with those adversely impacted by mining activities. The focus is on indigenous peoples, who especially suffer the consequences of an irresponsible energy transition and financial institutions as actors of critical importance for the energy transition. The article argues that the Netherlands should adopt a strong national implementing law that goes beyond the minimum requirements set by the EU, increasing the level of protection of people and the environment in CRM supply chains. Adopting such a due diligence law is essential for the Netherlands to fulfill its duties for human rights and the environment abroad, in the context of a just energy transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| 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".