Bibliographic record
Abstract
What explains the recent (perhaps temporary) resurgence of sophisticated maritime pirate attacks in Southeast Asia in the face of strong regional counter-piracy efforts? Given Southeast Asian countries' relatively well-functioning institutions, political, economic, and conflict-related explanations for the return of piracy are incomplete. As an innovative extension to structural arguments on piracy incidence, we take an approach that focuses on adaptation by the pirates themselves, using incident-level data derived from the International Maritime Organization to track how sophisticated pirate organizations have changed what, where, and how they attack. In response to counter-piracy efforts that are designed to deny pirates the political space, time, and access to economic infrastructure they need to bring their operations to a profitable conclusion, pirates have adapted their attacks to minimize dependence on those factors. Within Southeast Asia, this adaptation varies by the type of pirate attack: ship and cargo seizures have shifted to attacks that move quickly, ignore the ship, and strip only cargo that can be sold profitably, while kidnappings involve taking hostages off ships to land bases in the small areas dominated by insurgent groups. The result is a concentration of ship and cargo seizures in western archipelagic Southeast Asia, and a concentration of kidnappings in areas near Abu Sayyaf Group strongholds.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".