Regulatory Cooperation in International Trade and Its Transformative Effects on Executive Power
Bibliographic record
Abstract
As international trade receives the brunt of local discontent with globalization trends and recent changes by the Trump administration have put into question the viability of such trade arrangements moving forward, there has been a clear trend in using international trade fora for managing regulatory barriers on economic development. This paper will discuss this recent trend in international trade toward increased regulatory cooperation through the creation of formalized transnational regulatory bodies, such as the U.S.-EU Regulatory Cooperation Body that was being discussed in the TTIP negotiations and comparable ones in the Canadian-EU Trade Agreement as well as U.S.-Mexico and U.S.Canada Regulatory Councils. In examining the informal transnational regulatory networks that have emerged from trade integration, it becomes clear that fragmentation has created non-centralized avenues for dialogue among various stakeholders to influence domestic regulation, especially in areas of environmental regulation, energy, and sustainable development. The paper argues that this trend has led toward the institutionalization of regulatory cooperation through preferential trade agreements, rather than multilaterally. Transnational regulatory networks and more formalized means of regulatory cooperation have influenced the executive branch, traditionally charged with negotiating trade agreements, from lead negotiator to a "regulatory partner" working not only for reducing barriers to trade, but also more specifically for the streamlining of regulatory standards that impact costs of inputs along the supply chain. Given today's negative climate around globalization and recent U.S. initiatives to diminish the role of agencies all together to implement regulation, this trend could take yet another turn—one that centralizes decisions regarding regulation in the President and his cabinet.
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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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".