Bibliographic record
Abstract
U.S. agricultural land remains attractive to foreign investors. As of December 31, 2020, foreign direct investment in this property amounted to $4.5 million, which is three times higher than in 2014. Foreign individuals and companies currently held an interest in nearly 15.2 million hectares of US agricultural land. This represents 2.9 percent of all privately owned agricultural land in the United States. Almost half of the reported foreign interest holdings of U.S. land are arable land and pastures, with timber and forestland accounting for 46 percent of the total acreage, which are mainly used by timber and "green" energy companies. Canadian investors own the largest amount of reported foreign-held agricultural land, with 32 percent. Foreign persons from an additional four countries, the Netherlands, Italy, the United Kingdom, and Germany collectively held 31 percent of the foreign-held acreages in the United States. The state of Texas has the largest amount of foreign-held U.S. agricultural land. Maine has the second, Alabama - the third largest amount of foreign-held agricultural land. Three states collectively held more than 25% of the reported foreign-held agricultural land in the United States, the vast majority of which is forestland. Current law imposes no restrictions on the amount of private U.S. agricultural land that can be foreign owned. However, several states have imposed certain prohibitions or restrictions on foreign ownership, but do not significantly inhibit foreign farmland ownership, while most states expressly allow foreign ownership. The US government controls direct investment flows. Federal law requires foreign persons and entities to disclose to USDA information related to foreign investment and ownership of U.S. agricultural land. The Agricultural Foreign Investment Disclosure Act of 1978 (AFIDA) and its federal regulations, implemented by USDA, established a nationwide system for the collection of information pertaining to foreign ownership of U.S. agricultural land. At the federal level, the Committee on Foreign Investment (CFIUS) authorized to review certain transactions involving foreign investment in the United States in order to determine the effect of such transactions on U.S. national security.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.028 |
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".