Anticipatory governance for responsible investment in energy transition minerals in the Western Congo Basin
Bibliographic record
Abstract
Demand for Energy Transition Minerals and Metals (ETMs) for clean energy technologies is driving a new wave of investment in mining. Many African countries contain significant reserves of ETMs and are eager to exploit their mineral wealth for economic benefits. However, extracting these minerals comes with social and environmental costs, including the degradation of high biodiversity habitats. Without learning from the past and anticipating possible futures, the potential benefits from ETMs could be offset by harms to vulnerable people and nature. In this paper, we draw from experience in the resource-rich Western Congo Basin forests to consider lessons and opportunities for anticipatory governance of ETM extraction to contribute to just and sustainable development pathways. We identify and build on four existing initiatives in which practitioners, policy-makers, and researchers can proactively engage in to enhance capabilities in foresight, networked decision-making, learning mechanisms, and open mindsets to guide responsible investment. By drawing on lessons and anticipating futures, decision-makers can strengthen institutional capability to guide and benefit from the impending wave of mine and infrastructure development. We call for greater attention to anticipatory governance for responsible investment in ETMs that aligns with environmental stewardship and community wellbeing in the Western Congo Basin.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".