Bibliographic record
Abstract
This special focus provides a set of statistical knowledge on intra-Southeast Asian trade from the late eighteenth to the mid-nineteenth centuries, to better understand the ways in which Southeast Asia became integrated into both long-distance trade and intra-Asian trade.In so doing, it explores aspects of how and why some of the traditional trading networks of the region survived the Western impact and came to play a vital role in the process of regional integration.In 1985 Kaoru Sugihara suggested that there was a growth of intra-Asian trade in the period 1880-1913, under the impact of the Industrial Revolution in England and the subsequent diffusion of industrialization in Europe and the United States.Unlike other parts of the non-European world, he argued, the rate of growth of intra-Asian trade during this period was faster than that of long-distance trade between the West and Asia.Over the last quarter of a century, relationships between long-distance trade-which is the trade between the West (United Kingdom, industrial Europe, and the United States) and Asia-and intra-Asian trade-which is the trade between India, Southeast Asia, China, Japan, and other Asian countries-have been vigorously explored; and trends in Asian regional integration, reflected in intra-regional trade, migration, and remittances, have been highlighted.Among the major observations is that during the high colonial era, from 1870 to 1914, Southeast Asia experienced the highest rate of export growth among Asian regions through its incorporation into both world and regional economies at almost equal speeds (Sugihara 1985;1996;2005).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.485 | 0.335 |
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".