Bibliographic record
Abstract
Maritime piracy is a serious threat to international trade. Indeed, using Instrumental Variable Poisson Pseudo-Maximum Likelihood (IV-PPML) and PPML gravity models and using data on maritime distance and on piracy attacks over the period 2000–2016, it is estimated that an increase by 10 piracy attacks on the shortest maritime trade route between a country-pair results in a decrease in bilateral trade’s value by 2.8%. The impact, at 1.5%, is much smaller if the endogeneity of piracy attacks is not controlled for. Heterogeneity analysis reveals that successful attacks, attacks that involve violence, or attacks that target cargo are particularly detrimental to trade. This paper contributes to the literature by being the first to look at: non-Somali piracy attacks, different commodity groups, and various forms of attacks. This paper also proposes the use of maritime distance, instead of the commonly used great-circle distance. Finally, it offers a new instrumental variable for piracy attacks, namely, the sum of the square of the highest security apparatus index among countries in the vicinity of each vital chokepoint crossed by a ship travelling on the shortest maritime trade route between a country-pair, in a given year.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".