Bibliographic record
Abstract
In 1994, the North American Free Trade Agreement laid the groundwork for today's neo-liberal global trade order. Three years later, the US government's Framework for Global Electronic Commerce enshrined minimal government intervention and industry self-regulation for the tech industry, principles that have since defined US digital trade policy, culminating in the digital chapter of the United States-Mexico-Canada Agreement (USMCA). Digital technologies, central to commerce, governance and daily life, also pose numerous challenges. Competition authorities, privacy regulators and worker advocates now question the surveillance capitalist business model, yet trade rules continue to reinforce and legitimize it. Technology policy is a key focus of the ongoing negotiations over tariffs reinstated when US President Donald Trump took office in January 2025, which has provided an opening for tech companies to cast tariffs as market-correcting mechanisms. The formal review of the USMCA scheduled for July 1, 2026, represents a critical inflection point. This paper traces US digital trade policy from the Clinton-era vision of self-tech regulation to today's far-reaching digital trade framework. It reveals how current digital trade rules clash with contemporary economic and social priorities, as well as industrial policies, and warns negotiators that if North American trade is to deliver shared prosperity and technological development and protect worker well-being, digital trade should shift from embracing surveillance capitalism to restoring democratic accountability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".