Incorporating a "Best Interests of the Child" Approach Into Immigration Law and Procedure
Bibliographic record
Abstract
United States immigration law and procedure frequently ignore the plight\nof children directly affected by immigration proceedings. This ignorance\nmeans decision-makers often lack the discretion to protect a child from\npersecution by halting the deportation of a parent, while parents must\nchoose between abandoning their children in a foreign land and risking\nthe torture of their children. United States immigration law\nsystematically fails to consider the best interests of children directly\naffected by immigration proceedings. This failure has resulted in a split\namong the federal circuit courts of appeals regarding whether the\npersecution a child faces may be used to halt the deportation of a parent.\nThe omission of a "best interests of the child" approach in immigration\nlaw and procedure for children who are accompanied by a parent fails to\nprotect foreign national and United States citizen children. Models for\neliminating these protection failures can be found in United States child\nwelfare law and procedure, international law, and the immigration law of\nother nations, such as Canada. Building from these models, the United\nStates must implement and give substantial weight to the best interests of\ndirectly affected children in its immigration law and procedure.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".