An Agential Constructivist Analysis of Meaningful Stakeholder Engagement in Africa's Critical Minerals Sector
Bibliographic record
Abstract
Although transitioning to renewable energy sources is an important strategy to address climate change, relatively little attention has been allocated to how the supply chains associated with this transition is impacting community members who reside near the mining sites of ‘critical minerals’ – otherwise known as ‘green minerals’. Concomitantly, it is unclear whether the carbon footprint of all aspects of extracting and refining critical minerals, including its supply chains, produces a net gain in terms of addressing climate change. A similar calculation is murky as regards addressing governance challenges in mining sectors. Owing to its position as holding among the largest reserves of critical minerals, insights from the Democratic Republic of Congo (DRC) will be employed as a means of addressing the latter question. Based on recently conducted fieldwork, we find that the ‘voices’ of the very people living near where the mining occurs are rarely incorporated as part of these debates and problem-solving efforts. Guided by an agential constructivist theoretical approach and informed by participant observations and other primary data, we examine and compare the extent to which meaningful stakeholder engagement (MSE) regimes, such as the United Nations Guiding Principles on Business and Human Rights (UNGPs) and Africa Mining Vision (AMV), promote public goods envisioned by environmental and social impact assessments in the DRC in particular, and Africa more broadly.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".