Policies for High-Skilled vs. Low-Skilled Migration in North America
Bibliographic record
Abstract
Copyright © 2013 Camelia Tigau. This is an open access article distributed under the Creative Commons Attri-bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. High-skilled migration has always been given priority and even encouraged by international and national policy. At the same, international agreements for low-skilled migration tend to restrict it. Human capital is considered valuable according to educational levels and working abilities, which puts undocumented mi-grants in a difficult economic position. The NAFTA area is no exception: while the migration of profes-sionals is given priority by mechanisms like the TN and H1 visas, there is still no agreement to relieve the situation of unskilled immigration across the border from Mexico to the US and Canada in search of a better life. At the same time, migration between Canada and the US is much freer, even though profes-sionals are also given priority; after NAFTA, Canada has even experienced brain drain to the US. The main question is how to reduce unfair differences in policies that prioritize the mobility of certain indi-viduals based on their qualifications. Method: This paper analyzes and compares NAFTA’s impact on policies for high- vs. low-skilled migration. It also uses the author’s ethnographic studies to express the view of migrants on how human capital should be managed internationally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".