Bibliographic record
Abstract
As the activities of Canada-based multinational enterprises (MNEs) have fostered an impressive outflow of foreign direct investment (FDI) abroad, many empirical studies have been put forth to describe the characteristics and account for the reasons behind Canadian FDI. Yet, most of these Canada-based studies have relied on questionnaires (and surveyed only large MNEs) to fulfil data requirements and have given a less than complete view of Canadian MNE behaviour. A study that utilizes a larger data set (and is, therefore, not biased by company size or spatial area of consideration) is needed. To realize this goal, a sample of more than 4500 examples of Canadian FDI has been collected into a data set. From there, with the use of a regression analysis (and with considerable reliance on the resulting outliers), some of the determinants of Canadian MNE behaviour across the world and within the United States are uncovered. Spatially, the favourite target of Canadian outward FDI has been the United States and then the United Kingdom, but significant agglomerations of Canadian controlling capital can be found in many parts of the world (particularly in Western Europe, the Caribbean region, Australia, Brazil and in various Asian destinations). Canadian direct investment abroad is most attracted to: large foreign markets, countries that are well-established trading partners with Canada, and to favourable place-specific labour and aesthetics conditions. Evidence also suggests that countries with strong historical ties to Canada and a positive political attitude toward FDI are likely to receive a disproportionate amount of Canadian FDI as well. Also, distance from the Canadian border may bias some direct investment decisions into the U.S.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.695 | 0.545 |
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".