THE EXCEPTIONAL UNFAIRNESS OF THE “EXCEPTIONAL AND EXTREMELY UNUSUAL HARDSHIP” TEST
Bibliographic record
Abstract
Legislators often face criticism for introducing ambiguous terms into law. The "exceptional and extremely unusual hardship" (EEUH) standard in U.S. immigration law is one such prominent example. Delving into a historical analysis, the article tracks the evolution of the EEUH standard from its incorporation in the Immigration and Nationality Act of 1952 to its current applications. Through a comprehensive survey across different jurisdictions such as the UK, Canada, and Australia, the paper exposes the inadequacies of the EEUH standard, emphasizing its obsolescence. Advocating for a paradigmatic reassessment, it proposes the replacement of the EEUH standard with the “best interest of the child” standard. Central to this proposal is a firm conviction in broadening judicial considerations to encompass not only immediate removal scenarios but also the critical impacts on the mental and physical well-being of citizen children. Highlighting the importance of the child's citizenship status in removal deliberations, this perspective emphasizes the increasing recognition of the inherent rights vested in citizen children. Fundamentally advocating for a transformative shift in removal cases, it proposes a more inclusive and child-centric approach grounded in the best interest of the child standard.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".