Ecosystemic Approaches to Extractive Business and Human Rights Issues*
Bibliographic record
Abstract
The article by Étienne Roy Grégoire, Marc-André Anzueto, Bonnie Campbell, Mélisande Séguin and Nancy R Tapias Torrado uses the concept of an ‘extractive normative ecosystem’ to account for the interactions between the various norms, discourses and policies governing the relationship between extractive industries and local communities. It argues that the exploitation of natural resources significantly alters social relations and exacerbates the risks of human rights violations, particularly in the context of globalisation. The article critiques traditional approaches to Corporate Social Responsibility (CSR) and Business and Human Rights (BHR), and highlights the need for an ecosystem approach to understand the complex relationships between different regulatory regimes, including state laws, indigenous legal systems and international law. The text is divided into four sections: the theoretical implications of the ecosystem paradigm, recent legal developments in business and human rights due diligence legislation in Europe and Canada, the potential of applying an ecosystemic approach to the extractive sector, and a conclusion highlighting the challenges faced by communities affected by extractivism.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".