Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it