A New Approach to Global Studies from the Perspective of Small Nations
Bibliographic record
Abstract
With emphasis on East Asian and North American examples – notably Japan and Quebec – Date, Laniel and their contributors take a new approach to the understanding of small nations and their role in the international system. Small nations, by their very nature, raise significant questions about what a nation is. Some small nations are sovereign states with relatively small populations and limited territory, others are nations within larger sovereign states, with distinctive cultures, governance structures or other features that differentiate them from their “parent” state. By focussing on non-European nations in particular, the contributors to this volume challenge our conceptions of what a small nation is and how it operates within the international system. They focus in particular on the nation-within-a-nation-state of Quebec and on Japan, supplemented by further examples from East Asia. By interrogating what these examples have to show us about the typology and character of small nations, they offer a critique of superpower and draw out the potential of small nation studies. A valuable resource for students and scholars of international relations and theories of the nation and nation state. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".