Geopolitical Mining: From Ore to Order in a World of Engineer and Juridical States (White Paper)
Bibliographic record
Abstract
This paper defines Geopolitical Mining: a system in which minerals cease to be generic commodities and instead become instruments of state power. The leverage sits midstream (conversion, separation, QA/QC, and standards) not in ore alone. We contrast two reproducible governance logics: Engineering-centric systems compress latency from plan to plant via integrated delivery and concessional finance; juridical-procedural systems maximize ex-ante legitimacy through layered review and enforceable rights. Each logic creates distinctive strengths and failure modes across permitting, midstream build-out, and recycling. We propose a hybrid playbook that preserves voice and remedy while restoring clock certainty: parallel reviews with endpoints, bundled infrastructure decisions, FOAK lanes with enhanced monitoring, and blended finance gated by on-spec/utilization KPIs. Sector lenses (lithium, copper, rare earths) illustrate how “tempo + chemistry + legitimacy” determine who captures the marginal processed tonne. Appendices provide a permitting-clock template and a board checklist to translate policy into delivery and delivery into durable legitimacy. License: CC BY 4.0.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".