Issues, Trends and Challenges in an Emerging Global Power Structure
Bibliographic record
Abstract
The reality of the early 21st century is that the world is in the grip of the transformation of the power structure. China has risen into global reckoning; Russia began to rise from its inertia; North Korea has evolved to a global threat. All have begun to lay claims to a greater role in the international political system. The unipolarism of the post-Soviet era seems to be dissolving before our eyes. These emerging trends raise questions as to; what sort of multipolarism are we talking about? How will the coming multipolar order operate? Will great power be able to work together to uphold order? Will they descend into self centred and destabilizing military and economic competitions? Can the world support multiple world orders, co-existent yet separate? There are no iron-clad answers to these questions. However, current geopolitics does, perhaps, allow for a glimpse into the future. This article aims to contribute to that discourse by making three claims. First, there is a dramatic increase in the number of global actors. Second, the diversity among actors has created opportunities for the emergence of new systems and new partnerships and for old ones to be strengthened and transformed. Lastly, the future multi-polar world will be potentially more unstable than all the other multi-polar periods history has experienced: for the first time in history, the world could become both multi-polar and nuclear.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".