Visa to Stay: Immigration Reform for International Students in the United States: From Contractual Limits to Affiliation-Based Opportunities
Bibliographic record
Abstract
International student mobility is a vehicle of globalization in today’s world, with a significant rise in students pursuing higher education abroad over the past two decades, reaching approximately 6.9 million globally. Regardless of personal motivations, the decision to study abroad rests in a careful evaluation of whether long-term rewards outweigh the short-term sacrifices these students make. For students looking to build a professional foundation and immerse themselves in the culture of the country in which they study, few long-term rewards are more appealing than having their student visas serve as a pathway to permanent residency. Determining who may be granted permanent residence in a country is central to immigration law and an issue that continues to fuel extensive scholarly debate and theorization. Professor Hiroshi Motomura offers two lenses through which immigration law can be viewed: immigration as contract and immigration as affiliation. Immigration as contract conceptualizes the relationship between noncitizens and host countries based on mutual expectations and obligations. Conversely, immigration as affiliation acknowledges and values the evolving ties that noncitizens forge with their host communities, emphasizing integration and gradual detachment from their country of origin. The US, Canada, and the UK are among the top destinations for international students, and each country’s immigration framework aims to both promote diversity and strengthen the labor market. These goals often conflict due to concerns about immigrants displacing native workers. Consequently, due to this apprehension, the legislative framework surrounding the student visa in each country attempts to strike a balance in varying degrees. This Note examines the US’s approach to student visas, highlighting its reliance on the immigration as contract framework and contrasting it with the immigration as affiliation approach used by Canada and the UK. By adopting a strict contractual framework and restricting opportunities for students to obtain permanent resident status, the US fails to effectively balance promoting diversity with protecting its labor market.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".