Legal and Humanitarian Implications of the 'Zero Tolerance' Policy for Asylum Seekers: A Comparative Analysis of U.S. and Canadian Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study aims to analyze the asylum refuge “Zero Tolerance” policy of the U.S. and effects of its emergence in legal-political humanitarian approach in comparison with the asylum legal-political system of Canada. The U.S.’s 2018 “Zero Tolerance” policy was based on increasingly aggressive and anti-immigrant enforcement strategies including family separation, and criminal prosecution of asylum seekers that resulted in vast human rights violations. These policies violate fundamental tenets of international law, including non-refoulement, family unity and due process. It also compares the approach taken by the U.S. to the one taken by Canada’s asylum system, the latter being much more educative of the interests of international human rights standards, but also plagued by problems, from backlogs and delays to that whole Safe Third Country Agreement thing. The research quantifies the mental and physical toll on asylum seekers of U.S. policy detailing the way it has made it more difficult to obtain legal representation and diminished applicants’ overall well-being through qualitative analysis. The findings point to the need for a balanced approach that weaves together humanitarian principles with legal protections to ensure a fair, predictable, and humane system for seeking asylum. The study concludes, however, that governments in both Canada and the United States must reform their policies as Canada’s approach becomes a model for refugee protection, in order to uphold the rights and dignity of people seeking asylum.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it