Revisiting Protectionism in the Global Economy: Economic, Supply Chain, and Technological Implications of the 2025 U.S. Tariff Policies
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it