Bibliographic record
Abstract
The Agricultural Foreign Investment Disclosure Act of 1973 (AFIDA) established a mandatory reporting system, overseen by the USDA, that requires foreign entities to provide information on all U.S. agricultural and non-agricultural land in which they hold an interest. “Interest” is reported as a fee interest (legal possession of both the surface and mineral rights), partial fee interest (must state percent ownership), life estate, trust beneficiary, purchase contract, or other. “Other” includes leases that are 10-years or longer. The regulations exempt foreign entities with interests solely in mineral rights and leases of less than 10 years in duration from reporting. In this report, the terms “owner” and “ownership” will refer to all types of interest held by the foreign entity, including long-term leases. According to the most recent AFIDA report, foreign investment in agricultural lands has increased over the past decade. As of December 31, 2021, just over 30 million acres (3.1%) of agricultural land in the U.S. was held by foreign entities. Texas has the distinction of having the most acreage held by foreign entities (almost 5.3 million acres or 3.4% of Texas’ acreage) but Maine holds the number one spot for the highest percentage of agricultural land held by foreigners (3.6 million acres equating to 20.1% of Maine’s acreage). Canada holds the largest percentage of Maine’s foreign-held acreage for the purpose of timber production (Foreign Ownership and Holdings of U.S. Agricultural Land, 2023).
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".