Caught in the Immigration Cross-Fire: The Changing Dynamics of Congressional Support for Skilled Worker Visas
Bibliographic record
Abstract
This project examines the congressional politics associated with legislation on skilled foreign workers, specifically the H-1B visa which was created by the Immigration Act of 1990. It attempts to explain why legislative policies were successful on a small scale between 1998 and 2004 and completely unsuccessful after 2004. Specifically, this study is a longitudinal qualitative analysis that uses Krehbiel's pivotal politics model (1998), Cox and McCubbins' party politics models (2005; 2007), Sinclair's (2007) unorthodox lawmaking theory, and Gilmour's (1995) strategic disagreement model to explain four key periods of H-1B legislation: (1) the passage of the Immigration Act of 1990; (2) passage of stand-alone legislation from 1998 through 2002; (3) passage of legislation through the use of riders from 1998 through 2002: and (4) complete stalemate after 2004. Using polarization as the main independent variable to explain shifts in congressional behavior, this study attempts to explain why congressional behavior dramatically shifted from 1990 to date. It concludes with a comparison of similar policies in Canada and Australia in order to ascertain whether their legislative experiences on foreign skilled workers coincide or differ from that in the United States and attempt to understand why.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".