The Stake of Private Military Companies in Natural Resource Governance
Bibliographic record
Abstract
The rapid acceleration of climate change has increased the demand for critical minerals. Rare earth elements have emerged as a valuable commodity to the future of hybrid energy, with many reserves located in West Africa. Concomitantly, the world has seen a spike in private security, with a growing normalization of private military companies (PMCs) in state defence. Though not typically associated with natural resources, PMCs, most notably those of Russian descent, have been cited in resource commerce in Syria, Ukraine, and West Africa. The Russian PMC “Wagner” has most recently become a household name for cited indiscretions and humanitarian violations throughout Russia’s 2022 invasion of Ukraine. Thus, as we see increasing ties between The Wagner Group and various African countries, the future of the “green transition” appears eerily uncertain. The inevitable scarcity in implementing such a green transition poses huge questions for what length states will go to for resources, and who pays the price. This paper investigates the nexus between private security and natural resource governance by examining the case of the Wagner Group in Africa. It uses process tracing to compare the way states use PMCs, arguing that Wagner is an exceptional PMC in their strategic acquirement of natural resources, largely on behalf of the Kremlin’s will. I find that PMCs like Wagner are effective tools of surrogate resource nationalism due to their ability to appeal to covert state interests with legitimate promises and bonds formed, all the while maintaining no official state affiliation. As the climate worsens, PMCs like Wagner pose a legitimate threat to the future of scarcity, as water could be the next resource in competition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".