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Record W7054942206

Attracting the Best and the Brightest: The Promise and Pitfalls of a Skill-Based Immigration Policy

2006· other· en· W7054942206 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2006
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPanacea (medicine)Immigration policyGovernment (linguistics)Immigration lawImmigration reformFace (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

One question that recently received heightened attention from lawmakers is whether or not immigrants should be admitted to the United States less on the basis of family ties and more on the basis of the skills they can contribute to the U.S. economy. Today, the most common way permanent immigrants enter the United States legally is through sponsorship by a family member already in the country. By contrast, nations such as Canada, Australia, and the United Kingdom admit immigrants primarily for employment reasons, based on a point system. Points are assigned on the basis of educational level, professional skills, proficiency in the host country's language, and other qualities that increase immigrants' likelihood of integrating into the host country's labor market. Policymakers should investigate how a similar policy might work in the United States. Although some of the practices associated with a point-based immigration system might benefit the U.S. economy, policymakers should be careful not to assume that such a system would be a panacea for the widespread dysfunction of U.S. immigration policies. Among the findings of this report: Today, the government allocates 480,000 visas each year to family-sponsored immigrants and 140,000 visas to immigrants entering to work. Approximately 86 percent of permanent employment visas are reserved for immigrants who are highly skilled or hold advanced degrees. Given the realities of immigration to the United States today, the government would face several challenges to implementing a system that selects most immigrants based on their skills, because a point system would multiply the paperwork and bureaucracy. The waiting times for immigrants wanting to join family would grow even longer as consideration of their applications was delayed in favor of immigrants with skills the nation desires. A point system favoring high-skilled workers would not meet the demand for less-skilled workers in industries such as agriculture, construction, and services, especially as more native-born workers earn college degrees, and as the U.S. population ages and the pool of native workers shrinks. U.S. businesses might suffer under a skill-based point system that reduces the flexibility of the labor market. Instead of employers directly recruiting the immigrants they need, the government would take on the responsibility of filling labor gaps and determining the skills of immigrants entering the labor force. The danger in this is that shortterm labor shortages could take priority over building longer-term economic stability and growth. Canada's experimentation with its immigration system provides a valuable lesson for U.S. policymakers in considering if and how such a system could be implemented in the United States. Its experience indicates that any point system should not replace other systems, but rather serve as a complement to them.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.303
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.005
GPT teacher head0.255
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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