Grounding critical minerals in values-centred approaches for just sustainability transitions
Bibliographic record
Abstract
Abstract Growing demand for critical minerals to accelerate a global energy transition presents new challenges and opportunities for responsible mine exploration and mine development. Social innovations have not kept pace with investment in mineral development, putting the wellbeing and rights of Indigenous peoples and local communities at risk. Historical and ongoing injustices mean that without pro-active attention to these issues, critical mineral development may cause social and environmental harm and exacerbate existing inequalities. The 2022 Values Assessment conducted by the Intergovernmental Panel of Biodiversity and Ecosystem Services (IPBES-VA) shows that transitions towards just and sustainable futures depend on recognising and embedding diverse values into decision-making processes across all domains, including mining. Values-centred approaches seek to identify diverse and under-represented values and activate values-centred leverage points to overcome barriers, shift power, and explore alternative pathways towards justice and sustainability. In this paper, we argue that the governance of critical mineral development can learn from values-centred approaches to inform responsible and inclusive mining and exploration. We outline how values-centred approaches might help to address current shortfalls in mineral resource governance, and identify promising examples of activating values-centred leverage-points drawing from global examples. We critically advance values-centred approaches by considering power, knowledge, and capabilities, while also acknowledging the need for pragmatism in improving mineral resource governance. We identify potential change mechanisms for integrating values into policy and practice and call for more attention to plural values in the exploration and development of critical minerals for just transitions.
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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".