Crude Oil, Refined Strategy: How Foreign Energy Investments Influence Intervention Strategies in Civil Wars
Bibliographic record
Abstract
Conventional wisdom and popular myths suggest states will go to great lengths to secure access to oil and gas resources. Recent academic work reinforces this view, particularly in explaining why energy-importing states might intervene in the civil wars of their suppliers. However, much of this research has focused on the incentives for importers and overlooks the role of foreign energy investments. Do states with oil and gas investments in countries experiencing civil wars behave similarly? This dissertation addresses this gap by exploring how energy investments influence foreign military support strategies during civil wars. Rather than fully committing to intervention by deploying troops, states with energy investments tend to opt for indirect military support strategies such as providing weapons, training, or intelligence, driven by the long-term implications of upstream oil and gas investments. This strategic approach called “hedging” allows invested states to display commitment to their chosen side and signal restraint to the opposition. By adopting this approach, can better balance the tradeoffs of military support, manage their reputations with both sides of the conflict, and safeguard the longevity of their investments. To test this theory, I conduct a quantitative analysis of 94 civil wars between 1975 and 2017, and present two case studies of British and French military interventions in the Nigerian Civil War (1967-1970) using declassified archival evidence. The findings reveal that when countries experiencing civil war host foreign oil and gas resources, states with oil and gas investments are more likely to offer limited support to signal commitment and restraint for the purpose of safeguarding long-term investments. Even when the uncertainty of civil wars diminishes, the long-term risks to energy investments deter states from significantly altering their military support strategies. These results challenge conventional arguments that natural resources invariably lead to more conflict. Instead, they offer important insights into the nuanced and complex relationship between energy resources and conflict decision-making.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".