National Identity in a Multicultural Nation: The Challenge of Immigration Law and Immigrants
Bibliographic record
Abstract
Samuel Huntington's provocative new book Who Are We?: The Challenges to National Identity is rich with insights about the negative impacts of globalization and the burgeoning estrangement of people and businesses in the United States from a truly American identity. The daunting question posed by the title of the book is well worth asking. After commencing the new millennium with wars in Afghanistan and Iraq, U.S. military torture of Iraqi prisoners, indefinite detentions of U.S. citizens declared by the President to be "enemy combatants," and a massive domestic "war on terror" that has punished and frightened Arab, Muslim, and other immigrant communities, many Americans have asked themselves the very same question. Professor Huntington's fear is that the increasingly multicultural United States could disintegrate into the type of ethnic strife that destroyed the former Yugoslavia during the 1990s, or, in less dramatic fashion, divided Quebec for much of the twentieth century. Forming a cohesive national identity with a heterogeneous population is a formidable task but, as Professor Huntington recognizes, critically important to the future 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".