Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effects of culture on international trade. Design/methodology/approach A measure of the cultural distance is incorporated into the Gravity model to test the marginal effects of cultural variables on bilateral trade between Canada and 53 other countries. In addition to the cultural distance and economic factors, other control variables such as religion and language commonalities are included. Findings After controlling for the size of GDP and linguistic commonality, the effects of culture on international trade are found to be insignificant. The empirical analysis shows that while the linguistic commonality has positive implications for international trade, the cultural distance and religion commonality do not seem important. Research limitations/implications What is true for the Canadian international trade may not be true for other countries, especially for developing nations. Moreover, this study is limited to the Schwartz's cultural dimensions. Practical implications Managers should not stay away from culturally dissimilar partners as long as trade is economically beneficial. Instead, they should pay attention to training bilingual agents and standardizing trade procedures in order to streamline the negative effects of linguistic dissimilarity. Originality/value This study refutes the generally accepted idea that culture is subversive to any cross‐border business activity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| 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 | 0.000 |
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 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".