Bibliographic record
Abstract
Abstract Piracy has been an important and persistent feature of Asia’s maritime history. In fact, the largest pirate organizations in all of history were found in Asia. Although often regarded as the antithesis of trade, piracy is actually closely related to the world of commerce. Pirates were themselves often traders (or smugglers) and relied on merchants to outfit their ships and sell their plunder. Despite the obvious and primary economic dimension of piracy, pirates were also political actors. This observation is significant because piracy has traditionally been distinguished from other forms of maritime predation (especially privateering, but also naval warfare) by stressing its supposedly inherently private nature. In Asia, however, the history of piracy is very much defined by its political contexts. Pirates themselves formed polities, whether as part of established coastal communities or in their endeavors to build their own states. What is more, as was the case in Europe, pirates often colluded with territorial states that used them as an instrument of state power, in order to harass and weaken their rivals. The political dimension of Asian piracy has long been overlooked due to the preponderance of European concepts and sources, which tend to depict all Asians involved in maritime predation as mere criminals. More nuanced studies of Asian pirates, especially when based on non-European sources, promise fresh insights into the commercial, social, and political worlds of maritime Asia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".