Improving the U.S. Immigration System: Lessons Learned from the Diversity Visa, Family, and Merit-Based Immigration Programs
Bibliographic record
Abstract
The U.S. immigration system is the subject of an ongoing debate regarding necessary reforms to protect American national security and benefit all Americans economically. This thesis asks two questions: (1) How should the current U.S. immigration system be improved to address existing economic and national security concerns presented by legal immigration?, and (2) What elements from existing U.S. legal immigration programs, as well as from Canada’s and Australia’s legal immigration programs, can the United States incorporate in its revamped immigration policies? This thesis conducted a comparative analysis of the U.S. diversity immigrant visa and family-based immigration programs and existing merit-based immigration systems in Canada and Australia. The inquiry identified which of the aforementioned immigration programs have had a positive effect on their respective countries’ economies, based on levels of education and unemployment rates, and which immigration policies have resulted in fewer terrorist attacks by immigrants who come to each country, via relevant noted programs. This thesis found that although the U.S. diversity immigrant and family-based immigration programs are not perfect, they serve an important purpose and can be improved. This thesis recommends, among other things, introducing points-based human capital criteria into family-based immigration and instituting a five-year review of the U.S. immigration system.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".