Bibliographic record
Abstract
"The idea of multilateralism is not something that can be forced on states, nor does it come naturally to them." —Tom Keating Seeking Order in Anarchy offers insights into both the theoretical foundations and the real-world outcomes of multilateralism in world affairs. Recognizing that Tom Keating’s theories, though rooted in Canadian foreign policy, have a broader application in international relations, Robert W. Murray has assembled an array of theoretical interpretations of multilateralism, as well as case studies examining its practical effects. Drawing from the insights of fourteen noted scholars and featuring an essay from Tom Keating himself, this volume examines the conditions that encourage states to adopt multilateral strategies, and the consequences of doing so in the context of increasingly complex global politics. Seeking Order in Anarchy is an important book for scholars, graduate students, policy makers, and anyone interested in how multilateralism functions in today’s world. Contributors: Francis Kofi Abiew, Edward Ansah Akuffo, Greg J. Anderson, David R. Black, Duane Bratt, Antonio Franceschet, Paul Gecelovsky, David J. Hornsby, Tom Keating, Christopher J. Kukucha, John McCoy, Robert W. Murray, Shaun Narine, Kim Richard Nossal, Matthew S. Weinert
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".