Slaving, Colonial Diplomacy, and Resource Extraction in Seventeenth-Century Maritime Asia
Bibliographic record
Abstract
This study combines perspectives of social change and resource exploitation from two angles: the intercultural diplomacy conducted by the VOC in early modern maritime Asia, and the trajectories of slavery, slave routes, and zones of coerced labour in this macro-region. The use of enslaved people by European polities for production of cash crops and domestic work in maritime South and Southeast Asia has been widely researched. The local consequences of the widespread slaving need however to be better understood, not least in terms of the exploitation of natural resources and their entanglement with social change in the affected areas. The article discusses how early colonial diplomacy and treatymaking with indigenous societies in the period 1600-1700 had a role in shaping slave circuits while impacting on local economic systems. A combination of slaving and extraction of commercial items of ‘luxury’ type is often found in the diplomatic instruments. The study highlights the possibilities of Dutch contracts and agreements to trace historical processes in combination with other types of sources. By looking at negotiating practices, we can better understand the structures of geographical distribution of slaving activities, trading practices, forced deliveries of manpower, and resistance to enslavement. In sum, the consequences of enforced movement of people in the contact zones between colonial and indigenous groups.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".