‘Writing the Rules of the Global Economy’: How America Defines the Contours of International Investment Law
Bibliographic record
Abstract
Some characterize the standards of protection promoted by investment treaty law as a form of denationalized law. International investment law, it is said, is comprised of generic rules, representing norms of global good governance, and not those of any particular state. This paper argues that investment treaty standards exemplify, in Bourdieu’s terms, the fictitious universalization of the particular case. This is revealed in debates over conferring upon President Obama trade promotion authority to negotiate new mega-regional treaties, like the Trans-Pacific Partnership (TPP) and Transatlantic Trade and Investment Partnership (TTIP). Disagreement turned, in large part, on the degree to which commitments to foreign investors corresponded to rights found in the US Bill of Rights. Both sides were preoccupied with the question of whether standards for investor protection in mega-regionals looked sufficiently like US constitutional law. By evaluating the strictures of investment treaty protections and US constitutional rights, the paper generates evidence that investor protections, while resembling those rights, surpass them by a considerable distance. So long as the origins of the content of investment treaty commitments remain misrecognized, US political actors will continue hold a monopoly in determining the content of the universal.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".