Bibliographic record
Abstract
This book examines traditional balance of power theory from a political-economic perspective, using historical examples, to draw out distinctions between the liberal and realist approach and how this affects grand strategy. The realist view of the balance of power theory includes implicit assumptions that economic assets can be turned quickly into power, and that states always respond to threats quickly and only with a view to the 'short-run'. These assumptions drive many of the expectations generated from traditional balance-of-power theory, discouraging realists from looking at domestic sources of power, which in turn undermined their ability to frame strategic decisions properly. By thinking about how power must be managed over time, however, we can model the choices policy-makers confront when determining expenditures on defense, while keeping an eye on the impact of those costs on the economy. By emphasizing the role of the state, identifying different causal patterns in domestic politics, and demonstrating the importance of systemic competition, this book aims to establish why a neo-classical realist approach is not only different from a liberal approach, but also superior when addressing questions on grand strategy. This book will be of much interest to students of security studies, international political economy, grand strategy and IR theory in general. Mark R. Brawley is Professor of Political Science at McGill University, Montreal, Canada. He is author of several books on International Relations, specialising in the connections between political economic issues and security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".