Bibliographic record
Abstract
Causal relations between the growth rates of exports, imports, and the GDP of Canada and the United States are studied using the vector error correction (VEC) model. Utilizing time-series annual data (1948-1996), Granger causality tests are performed within the framework of the VEC model. Bidirectional causality is supported for Canada from the foreign sector to GDP and vice versa. A weaker relationship between the foreign sector and GDP is statistically supported for the United States. These results are also supported by comparing the total trade (exports plus imports) shares to GDP of the two neighboring economies. The Granger causality tests suggest that Canada is a more open economy than the United States and more trade dependent. Economic policies leading to economic growth and development have been studied by many economists for a long time. The literature in this area is rich; a number of candidate variables that may be related to economic growth have been considered and carefully examined. Some of these variables are investment, saving, inflation, inflation variability, governmental expenditures as a percentage of GDP, government deficit, and other mainly macroeconomic variables. Many economic models were constructed for the purpose of understanding economic growth and to shed light on this issue.' A group of economists has focused exclusively on the foreign sector, particularly on the relationship of exports, imports, and GDP growth. Emphasis on international trade dates back to the mercantilists more than two centuries ago. Mercantilists were firm believers that trade surpluses were the only favorable outcome for the domestic economy from international trade relations. Mercantilists supported export promotion and pro
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".