www.pewhispanic.org Modes of Entry for the Unauthorized Migrant Population
Bibliographic record
Abstract
Nearly half of all the unauthorized migrants now living in the United States entered the country legally through a port of entry such as an airport or a border crossing point where they were subject to inspection by immigration officials, according to new estimates from the Pew Hispanic Center. As much as 45 % of the total unauthorized migrant population entered the country with visas that allowed them to visit or reside in the U.S. for a limited amount of time. Known as “overstayers, ” these migrants became part of the unauthorized population when they remained in the country after their visas had expired. Another smaller share of the unauthorized migrant population entered the country legally from Mexico using a Border Crossing Card, a document that allows short visits limited to the border region, and then violated the terms of admission. The rest of the unauthorized migrant population, somewhat more than half, entered the country illegally. Some evaded customs and immigration inspectors at ports of entry by hiding in vehicles such as cargo trucks. Others trekked through the Arizona desert, waded across the Rio Grande or otherwise eluded the U.S. Border Patrol which has jurisdiction over all the land areas away from the ports of entry on the borders with Mexico and Canada. The Pew Hispanic Center has previously estimated that there are between 11.5 and 12 million
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.246 | 0.067 |
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".