Immigration from China to Canada in the Age of Globalization: Issues of Brain Gain and Brain Loss
Bibliographic record
Abstract
Historically, waves of immigrants who contributed to the labour supply needed for agricultural settlement and industrial expansion. Since the end of the Second World War, there has been a change in how immigrantreceiving countries have framed immigrant selection policies. As advanced industrial economies experienced an increased demand for skilled workers, the shift has been to an evaluation of the human capital and skill of prospective immigrants, rather than criteria based on national or racial origin. Under the influence of economic globalization, the recruitment of highly skilled workers has become a more pressing issue for immigrant-receiving countries like Canada and the US. The purpose of this paper is to examine the recent trends of immigration from China to Canada and to analyze the economic worth of human capital transfer to Canada. The paper provides estimates of the value of human capital transfer to Canada as a result of immigration from China, and assesses how the transferred human capital is being evaluated in the Canadian labour market. The analysis suggests that international migration in the global age involves the transference of human capital and the embedded economic value of such capital, but whether this capital is utilized is contingent upon how the labour market of the receiving country can fully recognize the productivity of such labour.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 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) | 0.002 | 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".