Bibliographic record
Abstract
Abstract Why do some states project military force to seek control of resources, while others do not? Conventional wisdom asserts that resource-scarce states have the strongest interest in securing control over resources. Counterintuitively, this book finds that, under certain conditions, the opposite is true. Perils of Plenty argues that what states make influences what they want to take. Specifically, the more economically dependent states are on extracting income from resource rents, the stronger their preferences to secure control over resources will be. This theory is tested with a set of case studies analyzing states’ reactions to the 2007 exogenous climate shock that exposed energy resources in the Arctic. This book finds that some states, such as Russia and Norway, responded to the shock by dramatically increasing their Arctic military presence, while others, such as the United States, Canada, and Denmark, did not. Contrary to the conventional wisdom, countries with plentiful natural resources, such as Norway and Russia, were more—not less—willing to back their claims by projecting military force. This book finds that plenty can actually lead to peril when states with plentiful resources become economically dependent on those resources and thus have stronger incentives to secure their control. These findings have implications for understanding both the political effects of climate change in the Arctic and the prospects for resource competition in other regions, such as the Middle East and the South China Sea
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".