Bibliographic record
Abstract
The field of International Relations was born out of the moral concern with the problem of war and the conditions that would be necessary and sufficient to bring something akin to universal peace. The political theories underlying international organizations share this perspective. The United Nations, for example, has as its main purpose the maintenance of “international peace and security.” Numerous books, following diagnostic exercises on the causes of war, advocate hegemony, balances of power, world government, or collective security as paths to peace. All have an implicit or explicit universal bias. Waltz (1959) provided a significant fillip to these disparate studies by systematizing causal relationships in the famous three levels of analysis: war as the result of human nature, the domestic makeup of states, and the international system. However useful as organizing devices, these explanations failed because they could not account for critical variations in the incidence of war. If war is rooted in a Hobbesian psychology, why has Sweden not been involved in a war since 1721? If war is the outcome of particular socioeconomic characteristics, as Marxist theorists proposed, why did many socialist states use military force with a higher frequency than most capitalist states? And if anarchy (the main system characteristic) remains the most important permissive cause of war, then why do we find war-free regions such as South America, North America, and Western Europe?
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".