Piracy in the Horn of Africa Waters: Definitions, History, and Modern Causes
Bibliographic record
Abstract
Throughout history, ocean piracy was common in different parts of the world, but it was rare in the Horn of Africa waters. Although international law clearly defines piracy, the term is often carelessly used interchangeably with different crimes (armed robbery, atrocities against the victims of shipwrecks, maritime terrorism, insurgent attacks, on sea intercommunal conflicts, and at times illegal fishing). In the first section, this article critically examines the link between the multiple definitions of the concept of piracy and how these can explain the various incidents that occurred historically on the coast of Somalia. In the second part, we explain different types of maritime attacks and criminalities that took place in the Horn of Africa waters prior to the upsurge of piracy in late the 1990s. We argue that all attacks and criminal incidents at sea cannot be classified as piracy. We explain why incidents of piracy were rare before the Somali state was established. Finally, utilizing Collier and Hoeffler’s greed and grievance theory, we seek to explain the factors that led to the emergence and spread of piracy. We contend that crime of opportunity explains in large part the motives of the pirates and their ringleaders while statelessness, poverty, illegal fishing, and toxic-waste dumping explain the initial emergence of piracy and tolerance for it among the coastal communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".