Attracting Minds: How Canada and China Make Pragmatic Use of Migration Policies to Foster Innovation
Bibliographic record
Abstract
One of the main features of the Westphalian state system is the degree of control nations have visa -vis their borders. Throughout the centuries, national governments were consolidated as the 'gatekeepers' of their territories, having the prerogative to determinate who and in which circumstances foreigners shall have access to their sovereign spaces. In recent decades, migration has emerged as a relevant subject in states' agendas. Despite the enactment of stricter entry policies in countries around the world, some states have aligned their prerogatives towards migration control with wider objectives, looking to attract high-skilled individuals to fill labor shortages or contribute with the development of strategic sectors and innovations. While Canada is known for welcoming migrants and highlighting the positive impacts newcomers have to its economy, China, known as a country of emigration, has also recently introduced policies for 'talented migrants'. Therefore, it is pertinent to understand how these two dierent countries, with very distinct societies and economies, make usage of their migration policies to boost innovation. This paper will conduct a comparative study on Canada's and China's policies aimed at attracting high-skilled workers, drawing attention to their similarities and discrepancies in order to better understand their respective objectives and consequences. The study will argue that, while Canada's policies congregate its inherent need for a larger population and its desire to foster innovation, China's model is much more selective and not aimed at promoting a population increase.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".