Understanding borders through dynamic processes: capturing relational motion from south-west China’s radiation centre
Bibliographic record
Abstract
This research supports the evolution of border studies by advancing recently elaborated theoretical insights convincingly captured as borders in motion and the multiscalar nature of borders. Drawing on ongoing empirical research in Yunnan province, China, the combined perspectives tender a new approach of ‘relational motion’ to inform analyses interrogating the complex and dynamic nature of border-related phenomena occurring in, across and beyond Yunnan. We propose a new consideration of the ways border phenomena manifest, and detail outcomes by highlighting a range of interrelated processes. Our focus on processes, as opposed to borders or borderlands only, provides a critical insight to understand how border-related phenomena shape – and are shaped by – a diverse range of initiatives whose nature are connective and discrete, distinct and obscure, theoretical and empirical. Theoretical discussion is centred on four empirical sections: Kunming as a border and borderlands metropolis; Yunnan as an integrative energy centre; Yunnan as a focal point for transregional security; and transborder migration in Yunnan. Empirical insights contextualize how macroscale initiatives, such as the Belt and Road Initiative (BRI)– manifest at multiple scales through border-related phenomena. The research presents fresh empirical and theoretical insights into China’s south-west to understand this dynamic, nuanced region better.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".