Bibliographic record
Abstract
Abstract This article reviews the Trump Administration’s impact on US investment policy – meaning, US policy regarding the promotion and protection of US investment abroad – at the close of the Administration. In short, the Administration’s impact on US investment policy was substantial by any measure. It withdrew the United States from the Trans-Pacific Partnership, which was effectively the largest investment agreement ever negotiated, and it also renegotiated the North American Free Trade Agreement and replaced it with the United States-Mexico-Canada Agreement (USMCA), which features a novel approach to investor-state dispute settlement. The Administration’s impact on US investment policy was also shaped by steps it chose not to take, including its decision not to replicate its approach in the USMCA in its amendments to the Korea-United States Free Trade Agreement, re-start the United States-China Bilateral Investment Treaty or Trans-Atlantic Trade and Investment Partnership negotiations or re-visit dozens of US investment agreements that are currently in force. This article reviews these decisions with a view to considering the challenges and opportunities that await future US policymakers in the executive and legislative branches with respect to US investment policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".