What factors matter for trade at the global level? Testing five approaches to globalization, 1820–2007
Bibliographic record
Abstract
This article uses a global-level dataset with information across nearly two centuries to explore the factors associated with the expansion of international trade. The results of the autoregressive conditional heteroskedasticity regressions show that trade since the early 1800s is strongly coupled with advancements in industrial technology and its ability to cut the cost of commodities transport. In addition, the spread of democracy and the geopolitical stability promoted by the hegemonic nation-state are additional factors that augment trade during the past two centuries. The findings also reveal that trade since the early 1900s is further enhanced by the growing membership base of the United Nations and the World Trade Organization, given their propensity to generate compatible national institutions and a uniform set of rules for cross-national commodities exchange. However, there is no support for the claim that advancements in communications technology or the expansion of international governmental organizations increases trade globalization.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".