Chinese Foreign Direct Investment in North America: Comparing Canadian and U.S. Attitude
Bibliographic record
Abstract
As China’s economy becomes larger—naturally, the global outflow of China’s foreign direct investment (FDI) has also been increasing at a rapid pace. One of the most popular regions for Chinese investment today is North America. Yet despite China’s great enthusiasm to invest in Canada and the US, Chinese firms have received much antagonism in North America. Often times, Chinese acquisitions are viewed in a negative light, and are even denied on grounds which appear to be erroneous. This study asks an important question: what are the political reasons and conditions behind the acceptance and rejection of recent Chinese FDI in North America? To answer this question, this study analyzed and compared Canada and the US in great detail. By observing the sectorial distribution of Chinese FDI, the institutional constructs, as well as the most controversial cases of Chinese takeovers in both countries, the study has found that hegemonic competition and institutional structure plays a major role in the evaluation of Chinese FDI. Hegemonic competition creates the perception that Chinese FDI is a threat in the US, while the institutional structure in US allows the negative perceptions of China to influence the FDI evaluation process. Derived from the two major factors, secondary factors such as the policy preference of lawmakers, as well as the type of FDI itself are also important determinants of Chinese FDI in North America. As a result, Chinese FDI is more likely to be denied in America. While in Canada, due to the absence of a Sino-Canadian rivalry, Chinese FDI is perceived with more normalcy. Hence, Chinese FDI is less likely to be denied in Canada.
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 | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".