COMPETING FOR SKILLS: US IMMIGRATION POLICY SINCE 1990
Bibliographic record
Abstract
MMIGRATION policies are the mix of international, national, and local rules and programs that aim to facilitate the admission and integration of some foreigners and prevent the entry and stay of others.This article examines U.S. policies on legal immigration, with particular focus on the admission of highly skilled migrants.The United States is a nation of immigrants.Americans share a common experience: they or their forebears left another country to begin anew in the United States.Historically through the turn of the twentieth century, immigrant newcomers came in waves that reinforced the national origin of their forebears, and the government minimally regulated numbers or skills.That changed with legislation in the 1920s that introduced restrictions based on national origins, followed by slow post-World War II policy liberalization and the genesis of today's admission policies in the 1960s.Now policymakers debate the merits of admitting immigrants primarily for their family ties, which essentially reinforces national origin, compared with an emphasis on immigrants' skills.Immigration to the United States has been of such volume and diversity that there is an intrinsic acceptance, at times reluctantly, of the role immigrants from diverse places play in constructing the "American."Certainly, Canadians see themselves as a land of immigration, but the British and French enterprises in Canada displace immigration as the founding myth of the "Canadian" (Hawkins 1988:34).These national differences have contributed to different historical justifications for similar, exclusionary, admission policies.In either case, early U.S. and Canadian immigration or admission policies from the turn-of-the-century were explicitly linked to immigrant or integration policy.Exclusionary policies and restrictions on the number of immigrants were devised to address integration concerns.Significant changes ensued in the two decades following the Second World War.The post-war policies repealed exclusion-
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".