The Race for Talent: Highly Skilled Migrants and Competitive Immigration Regimes
Bibliographic record
Abstract
The United States has long been the ultimate IQ magnet for highly skilled migrants. But this trend has changed dramatically in recent years. Today, the United States is no longer the sole - nor the most sophisticated - national player engaged in recruiting the best and brightest worldwide. Other attractive immigration destinations, such as Canada, Australia, and the United Kingdom, have created selective immigration programs designed to attract these highly skilled migrants. Professor Shachar analyzes this growing competition among nations, referring to it as the race for talent. Whereas standard accounts of immigration policymaking focus on domestic politics and global economic pressures, Professor Shachar highlights the significance of interjurisdictional competition. This new perspective explains how and why immigration policymakers in leading destination countries try to emulate - or, if possible, exceed - the skilled-stream recruitment efforts of their international counterparts. These targeted migration programs increasingly serve as a tool to retain or gain an advantage in the new global economy. Indeed, countries are willing to go so far as to offer a talent for citizenship exchange in order to gain the net positive effects associated with skilled migration. Such programs are clearly successful, as evidenced by the increase in the inflow of highly skilled migrants to those countries. Simultaneously, emigrants' home nations have engaged in efforts to reap a share of the welfare-enhancing contributions generated by their highly skilled emigrants, including redefinition of the nation's membership boundaries. This consequence of the race for talent raises significant questions about the relations between citizenship and justice, as well as mobility and distribution, on a global scale. For the United States, which has traditionally enjoyed an unparalleled advantage in recruiting global talent, these new global challenges come at a difficult time. They compound long-standing problems in America's immigration system, which have only become more pronounced in the post-9/11 era.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 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".