Bibliographic record
Abstract
Transforming regions into pluralistic security communities, moving from zones of war to stable zones of peace, has assumed increasing importance as the weapons of war have become more deadly. We have been fortunate thus far. Early predictions regarding the spread of nuclear weapons proved false. Far fewer states have acquired weapons of mass destruction than was feared in the 1960s when the Nuclear Non-Proliferation Treaty was being negotiated, and there were notable cancellations of the nuclear programs of Argentina, Brazil, and South Africa. It cannot be assumed, however, that this good luck will continue, as news regarding Iran and North Korea indicates; and the war centered on the Congo shows that even conventional weaponry can be terribly lethal. It is important, therefore, to identify means of preventing military conflict. The end of the Cold War provides an opportune time to address this issue. In this chapter I use the liberal-realist model (LRM) of armed interstate conflict to identify the most promising means for transforming regions into zones of peace. The LRM is derived from social scientific research conducted over the past twenty years on the causes of militarized disputes and war. Earlier quantitative investigations proved disappointing. Studies in the 1970s of neorealist claims regarding the effect of the distribution of capabilities among the major powers were contradictory, and research on the greater peacefulness of democratic states was inconclusive. Many despaired of applying scientific methods to the study of international politics. There has been rapid progress in the last two decades, however, in statistical research on the causes of war by examining the behavior of many pairs of states through time. Stuart Bremer’s work with the LRM was path-breaking. Here, following Bremer’s lead, I analyze time series for over 12,000 pairs of states, 1885–2001, using the same techniques employed by medical epidemiologists. The results confirm that democracy and economic interdependence have important pacific benefits. Major elements of realism are also supported, but they do not provide a path to peace. Finally, I show that alternative explanations of interstate conflict – the clash of civilizations and hegemonic stability theory – are not consistent with systematic analyses of world history over the past one hundred years and more. The best hope for world peace is to continue to encourage liberal reforms: the institution of democracy and nations’ participation in the global economy. Fortunately, globalization has rapidly advanced in recent decades, and the prospects for continued expansion look good.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".