Attracting and retaining highly educated individuals: Two examples of immigration policies
Bibliographic record
Abstract
Ensuring more favourable conditions for immigration and circulation of the most educated structures of the foreign-born population has been rapidly becoming one of the most important goals of immigration policies in the economically developed countries. The availability of human capital is the basic precondition for the continuous economic development of every country. Therefore, the aim of the paper is to examine two successful examples (USA and Canada) of legal solutions to immigration policies for attracting and retaining professionals and highly educated individuals. Their bases are embedded in public policies relating immigrants of the majority of countries, both traditionally immigrant countries and the ones that have turned into immigrant countries. The USA and Canada are selected because they had relatively simple and quick procedures for granting immigrant visas back in the 1990s, which enabled a significant number of our highly educated citizens to immigrate to these two countries after the breakup of the former Yugoslavia. Immigration to the USA is based on a system of preferences and it relies significantly on the selection of immigrants based on the needs of the labour market. Canada?s example shows how through efficient development and in a relatively short period of time, the immigration system has been perfected by scoring, i.e. assessing the potential of human capital as the basic precondition for selecting potential immigrants. At the same time, the rapid development of the multiculturalism policy has created opportunities for successful long-term integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".