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Record W2953283845 · doi:10.1080/10242694.2019.1627511

Maritime Piracy and International Trade

2019· article· en· W2953283845 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDefence and Peace Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsEndogeneitySomaliInstrumental variableGravity model of tradeInternational tradeVariable (mathematics)Bilateral tradeEconomicsGeographyEconometricsMathematics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.246
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it