Great Games, Local Rules: The New Great Power Contest in Central Asia
Bibliographic record
Abstract
The struggle between Russia and Great Britain over Central Asia in the nineteenth century was the original game. But in the past quarter century, a new game has emerged, pitting America against a newly aggressive Russia and a resource-hungry China, all struggling for influence over one of the volatile areas in the world: the long border region stretching from Iran through Pakistan to Kashmir. In Great Games, Local Rules, Alexander Cooley, one of America's most respected Central Asia experts, explores the dynamics of the new competition over the region since 9/11. All three great powers are pursuing important goals: basing rights for the US, access to natural resources for the Chinese, and increased political influence for the Russians. But Central Asian governments have proven themselves powerful forces in their own right, establishing local rules that serve to fend off foreign involvement, enrich themselves and reinforce their sovereign authority. Cooley's careful and surprising explanation of how small states interact with great powers in this vital region greatly advances our understanding of how world politics actually works in this contemporary era. Available in OSO: http://www.oxfordscholarship.com/oso/public/content/politicalscience/9780199929825/toc.html
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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 | 0.008 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".