Bibliographic record
Abstract
Our peaceful trading partners are not our enemies; they are our allies. We should beware of the demagogs who are ready to declare a trade war against our friends—weakening our economy, our national security, and the entire free world—all while cynically waving the American flag. The expansion of the international economy is not a foreign invasion; it is an American triumph, one we worked hard to achieve, and something central to our vision of a peaceful and prosperous world of freedom.[1] – President Ronald Reagan Americans have long been skeptical of foreign investment in American companies. Since the end of the Second World War, Congress and Presidents have utilized the Committee on Foreign Investment in the United States (CFIUS) to monitor foreign investments in the U.S. with national security conc-erns. However, determining what is “foreign” for CFIUS review purposes is not a straight-forward analysis given increasingly complex financing structures. This Note traces developments in case law from an early twentieth-century case involving the treatment of a “colorless” corporation wholly owned by African-Americans, to mid-century Trading With the Enemy Act cases during the Second World War, to a more recent case involving an American company wholly owned by Chinese nationals. Modern courts have found that corporations can take on the race or national identity of their founders or investors, which, as this Note describes in greater detail below, represents a shift in how the courts view corporat-ions. Additionally, this Note describes the inadvertent foreign person problem where a corporation majority-owned by Americans, incorporated in America, and solely operated in America could become foreign for CFIUS purposes if the corporation received a substantial amount of foreign investment. This Note will recommend that CFIUS stop using the foreign control analysis as a gatekeeping function. Instead, CFIUS should shift the foreign control analysis to the formal review stage and use the scale of foreign control as informative rather than dispositive. This solution addresses national security concerns, promotes efficiency and effectiveness for all three branches of government as well as private industry, and adheres to American free-market and anti-discriminatory policies. [1] President Ronald Reagan, President of the U.S., Radio Address to the Nation on the Canadian Elections and Free Trade, Ronald Reagan Presidential Library & Museum (Nov. 26, 1988), https://www.reaganlibrary.gov/research/speeches/112688a.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.232 | 0.082 |
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".