Chinese Business Migration to Australia, Canada and the United States: State Policy and the Global Immigration Marketplace
Bibliographic record
Abstract
This paper examines business migration to Australia, Canada and the United States by integrating the concepts of a global immigration marketplace and the commodification of citizenship into global political economy theory. It finds that state business migration policies constitute “offers” to potential businesspersons, in a discourse of “competition” and simultaneously regulate the process. In the sorting process of potential migrants across countries many businesspersons have a rational “choice” of the country they want to emigrate to. This competition and choice provide evidence of a global immigration marketplace and data show that only Australia and Canada are active competitors with Canada having an advantage. An analysis of Chinese business migration from China, Hong Kong, Macao, Taiwan, Malaysia and Singapore indicate substantial numbers in the tens of thousands in the early 1990s but this has decreased in recent years due to several economic and political factors. Currently there are moderate levels of Chinese business migration with China now as the major source country. Since businesspersons are not a homogenous group the paper concludes with some suggested policy changes to make business migration more accessible and transformative.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| 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".