Bibliographic record
Abstract
In this paper we examine the effect of geopolitical risks on globalization using Pseudo-Poisson Maximum Likelihood (PPML) methodology for gravity trade model. As a measure for globalization, we use bilateral foreign direct investment (FDI) data from 2001 to 2012 and bilateral trade data from 1948 to 2019. Ours is the first paper making use of the recently created geopolitical risk (GPR) dataset using text analysis to test its effect on globalization. For the univariate model, we find a significant decline in FDI by 3.6% and 0.5% in trade for 10% increase in geopolitical riskiness, but for multivariate model we only see a significant increase in trade by 0.04%. We test the robustness of our results by doing more granular analysis by using individual country GPR measures as well as using KOF Globalization Index instead of FDI and Trade flow. Here also we see a significant negative effect of geopolitical risk on globalization.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".