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Record W7128600010 · doi:10.7225/imla30.25

TRUMP'S NEW TARIFF POLICY AND ITS IMPACT ON THE SHIPPING INDUSTRY

2025· article· W7128600010 on OpenAlexaboutno aff
Qi Chen, Amanda Pang

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsTariffGovernment (linguistics)Production (economics)

Abstract

fetched live from OpenAlex

President Donald Trump has levied new tariffs on all imports to the USA, triggering immediate retaliations from other nations, including the European Union (EU), Canada and China.Tariff wars are expected to impose negative impacts on the international trade and shipping industry, as higher tariffs will likely result in rising operational costs, decreased demand for shipping services, and reduction in long-term sustainability of the maritime industry.This paper presents various detrimental consequences that Trump's U.S. tariff policy has inflicted on the maritime industry.The paper also argues that new tariffs may help accelerate the supply chain diversification, as companies become fiscally motivated to creatively dodge excess tariffs.For example, companies may decide to partially move production to countries not on the countryspecific list of higher tariffs.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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