Bibliographic record
Abstract
This article proposes that the current immigration policy does not work and should be replaced with a universally enforceable immigration policy that can be successfully implemented. Because of limited resources, the new immigration policy must balance the following issues: the rule of law, human rights, national security, the economy’s dependence on cheap migrant labor, the costs and benefits of Mexican migrant workers, and the meaning of being an American. The author recommends decoupling national security and immigration policies, reducing spending and focus on militarization of the border, establishing a path for legalization of undocumented workers, and resuming bilateral talks with Mexico. The article discusses the proposed fence along the United States-Mexico border. The author explains this fence does not address the immigration concerns with the Canadian border, sea borders, or international airports. Next, the author explains how current immigration policy does not follow the rule of law. The author discusses the trade-offs we face in the implementation of a sound immigration policy. The author discusses human rights concerns arising from the current immigration policy. The author provides a history of immigration reform from the 1800s to the present day. The author concludes with recommendations for a better immigration policy.
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.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".