Superstar Search: Studying the Current and Potential Populations of Canadian Exporters and Foreign Direct Investors Abroad
Bibliographic record
Abstract
In this article, we analyze the potential to increase Canada’s exports and foreign direct investment abroad. To do so, we construct a unique administrative dataset containing detailed information for millions of companies that operated in Canada between 2010 and 2015. This allows us first to study the current population of Canada’s exporters and foreign direct investors abroad. Then, using probit modelling and propensity score matching, we infer the potential populations of these firms and examine their observable characteristics. Our estimates suggest there is considerable untapped potential to grow Canada’s outward international activity, with thousands of firms identified as high-potential exporters or foreign direct investors abroad. On a per-firm basis, the initial international activity of potential entrants is likely to be considerably lower because they tend to operate at a smaller scale than companies that are already internationally active—for exporters, less than half the scale, and for outward investors, less than one-tenth the scale.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".