Revisiting Protectionism in the Global Economy: Economic, Supply Chain, and Technological Implications of the 2025 U.S. Tariff Policies
Bibliographic record
Abstract
The rise of protectionist policies, such as the 2025 U.S. tariff increases, marks a clear shift from globalization and creates complex challenges for global trade. This study evaluates the impacts of these policies on economic stability, supply chains, geopolitical tensions, and technological advancements. Using a multidisciplinary approach- political economy analysis, scenario modeling, and actor-network mapping- it explores both macro and micro effects. Findings reveal significant economic disruptions, including decreased trade in sectors like automotive and electronics, and inflation affecting U.S. households. Supply chains are restructuring as businesses relocate manufacturing to Southeast Asia and implement AI-driven logistics for resilience. Tensions have risen from retaliatory actions by partners like China and Canada, heightening market instability. Innovations like blockchain and AI logistics are key to mitigating these challenges. The study offers insights for policymakers and businesses on balancing protectionism with global collaboration while addressing issues like inflation and job losses. It guides diversification of supply chains and the use of emerging technologies for effective risk management. By presenting a framework for understanding modern protectionism, this research calls for more investigation into sustainable economic strategies in a fragmented world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".