Perceiving the Silk Road Archipelago: Archipelagic relations within the ancient and 21st-Century Maritime Silk Road
Bibliographic record
Abstract
This paper analyses the ancient Maritime Silk Road through a relational island studies approach. Island ports and island cities represented key sites of water-facilitated transport and exchange in the ancient Indian Ocean and South China Sea. Building our analysis upon a historical overview of the ancient Maritime Silk Road from the perspective of China’s Guangdong Province and the city of Guangzhou, we envision a millennia-long ‘Silk Road Archipelago’ encompassing island cities and island territories stretching across East Asia, Southeast Asia, South Asia, West Asia, and East Africa. Bearing in mind the complex movements of peoples, places, and processes involved, we conceptualise the ancient Maritime Silk Road as an uncentred network of archipelagic relation. This conceptualisation of the ancient Maritime Silk Road as a vast archipelago can have relevance for our understanding of China’s present-day promotion of a 21st-Century Maritime Silk Road as part of the Belt and Road Initiative. We ultimately argue against forcing the Maritime Silk Road concept within a binary perspective of essentialised East-West conflict or hierarchical relations and instead argue for the value of a nuanced understanding of relationality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".