Bibliographic record
Abstract
IDWAY through the first decade of the twenty-first century, the United States was widely perceived to be experiencing an immigration crisis. As of 2006, three-quarters of all Americans rated immigration as a moderately big or very big national problem; more than half (54%) said that the United States needed to be protected against foreign influence; and nearly half (48%) said that newcomers from other countries threaten traditional American values and customs. Given the hysteria surrounding immigration and border control, it is not surprising that Americans drastically overestimate the relative number of immigrants present in the country. Although the true percentage of immigrants in the U.S. stands at around 12%, some 53% of Americans polled thought that it was 25% or greater.2 Readers have no doubt noticed that in the title to this essay I place the word in quotation marks. I do so not to downplay the scale of the recent increase in immigration or to minimize the severity of its associated problems, but to signal that to the extent a crisis exists, its origins are different from those imagined by most Americans and that it is very much a situation of our own making. Of immigrants present in the United States today, about a third are from Mexico, and among those living here in undocumented status the figure is closer to 60%.3 After Mexico, the next most important source for undocumented
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".