Bibliographic record
Abstract
The concept of "world order" is pivotal in understanding international relations and global dynamics. This bibliometric study maps the landscape of world order research, analyzing publication trends, intellectual structures, and future directions from 1990 to 2023. Using the Scopus database, 6762 relevant documents were identified and analyzed through keyword, co-authorship, co-citation, and bibliographic coupling analyses. The study highlights the increasing volume of publications, with major contributions from the United States, the United Kingdom, and Canada. Leading journals such as "International Affairs" and "Third World Quarterly" are key platforms for world order discourse. Influential works by scholars like G.J. Ikenberry and Amitav Acharya have significantly shaped the field. Several thematic clusters were identified, focusing on polarity, power dynamics, economic shifts post-2008 financial crisis, and China's rising influence. Future research should explore the evolving multipolar world order, the role of emerging powers, and the impact of technological advancements on geopolitical stability. This analysis not only synthesizes existing literature but also provides a conceptual framework for future research, addressing gaps and proposing new directions in the study of world order.
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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.167 | 0.266 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".