Speaking Foreign Languages in the United States: Correlates, Trends, and Possible Consequences
Bibliographic record
Abstract
With President George W. Bush's unprecedented call in January 2006 to expand the foreign language capacity of the United States, it has become clear that languages other than English (LOE) are of great interest to public policy in the United States. Yet the language capacity of the United States remains poorly documented. The 2000 General Social Survey (GSS) included new questions concerning the languages spoken by 1,398 respondents. Although about one quarter (26%) of respondents to this GSS sample claimed they could speak another language, only 10% overall said they could speak it very well. Those respondents who speak a foreign language were typically aged 25–44, graduate school educated, self‐identified as being of a race other than White, and living in large metropolitan cities and on the coasts. Spanish (50%), French (15%), and German (9%) were the most common languages spoken by the survey respondents. Whereas 67% of respondents who learned the language at home as a child said they could speak it very well, only 10% of those who learned it in school or elsewhere did speak it very well. As expected, LOE speakers gave significantly more responses revealing support of LOE and policies favorable to immigration, with LOE‐home speakers being more positive about these issues than LOE speakers who learned the language at school. These findings can help to inform national policy debates concerning how best to address the language needs of the United States.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".