Assessing Canada's Ability to Compete for Foreign Direct Investment
Bibliographic record
Abstract
The main purpose of this report is to assess Canada’s performance in attracting foreign direct investment inflows. The study reviews the literature on the benefits of FDI, analyses global and Canadian trends in FDI, identifies various factors affecting the inflow of FDI, and details how Canada ranks relative to other major OECD countries on the most influential factors. Canada’s share of world FDI has fallen markedly since 1980. The report finds that this development reflects the opening of other countries to FDI rather than a hostile climate for FDI in this country. Indeed, there is no one factor that can be identified as seriously impeding the flow of FDI to Canada. The report identifies a number of areas where Canada can potentially improve its attractiveness to FDI, including possible changes to FDI regulation, a more competitive tax regime, better infrastructure, and certain improvements in the human capital area.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".