Redefining the Safe Third Country Exception of the Immigration and Nationality Act in the Wake of Trump
Bibliographic record
Abstract
The U.S. Immigration and Nationality Act lays out when an asylum seeker has the right to apply for asylum in the United States. This right is not available, however, when an asylum seeker passes through a designated Safe Third Country. A Safe Third Country is an internationally used concept that, pursuant to an international agreement, requires refugees to seek asylum in the first safe country that they step foot in. As the Safe Third Country exception on the Immigration and Nationality Act stands now, there are no guidelines on how to evaluate whether a country is in fact safe. This allows for any presidential administration to subvert our commonsense notion of what safe is in an effort to reduce asylum claims and appear strong on immigration. Most recently, the Trump administration distorted the Safe Third Country Exception to that end. Drawing on Hungarian Law, Canadian Law, and the United Nations High Commissioner for Refugees, this note proposes that clear guidelines must be woven into the Safe Third Country Exception, so that the United States Attorney General can better determine if a country is in fact safe for asylum seekers. This would better prevent a presidential administration from subverting the idea of what a safe country is, while still allowing Safe Third Country agreements to be humane, effective, and diplomatic tools to distribute asylum claims.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".