Bibliographic record
Abstract
High-profile recent conflicts involving lucrative natural resources in such countries as Angola and Sierra Leone have drawn increasing attention to the link between natural resources and violence. While recent strategic, media, and academic attention has understandably focused on Iraq, the United States currently imports 15 percent of its crude oil from Africa, a figure that is forecast to increase to 25 percent by 2015. The Gulf of Guinea is poised to grow in strategic importance for the United States, and senior military and diplomatic officials are reportedly in advanced discussions with Sao Tome e Principe about establishing a regional U.S. Navy base there. This article argues that natural resource–related conflicts in places like West and Central Africa are not well understood. While such conflicts are unlikely to pose substantive operational risks to U.S. military forces, a failure to understand the dynamics underlying them risks exposing U.S. forces to smaller-scale Somalialike military problems and, perhaps more importantly, to serious public relations and reputational risks. One of the factors that makes natural-resource conflicts especially noteworthy is the alleged role played in them by leading private-sector actors. The sovereign governments of Angola and Sierra Leone both hired the services of Executive Outcomes, a private military company. De Beers has faced mounting pressure over its purchase of diamonds from these Scott Pegg is assistant professor in the Department of Political Science at Indiana University Purdue University Indianapolis. He received his Ph.D. in political science in 1997 from the University of British Columbia in Vancouver. Dr. Pegg previously taught in the Department of International Relations at Bilkent University in Ankara, Turkey. He is the author of International Society and the De Facto State (1998) and the co-editor of Transnational Corporations and Human Rights (2003). He has also published journal articles in the Washington Quarterly, Security Dialogue, and Third World Quarterly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".