Strategic Narratives in Global Trade Politics: American Hegemony, Free Trade and the Hidden Hand of the State
Bibliographic record
Abstract
The characterization of the US as a liberal hegemon seeking to uphold free-market capitalism against the illiberal state capitalism of China and other emerging powers has become commonplace, along with the attendant notion that American economic openness has been exploited by the unfair trade practices of other states. Given their dominance in US political discourse and role in shaping contemporary policy – including fueling Trump’s trade wars and aggressive unilateral trade actions against all of the US’s major trading partners, his attacks on the WTO, and talk of a “new Cold War” between the US and China – these claims are in need of far greater scrutiny. In this article, I challenge the stark dichotomy frequently drawn between American “free-market capitalism” and the “state capitalism” of its emerging challengers, arguing that this forms part of a strategic narrative deployed for political purposes, including legitimating the US’s use of aggressive trade policy measures. An examination of the American hegemon’s actual trade and industrial policies complicates this characterization. Despite presenting itself as a promoter and defender of free trade, I show that the unifying logic of US trade policy has always been the promotion of American economic interests: the US has engaged in considerable state intervention and trade protectionism, both to shield vulnerable industries and support others to achieve and maintain their global dominance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".