Googling the WTO: What Search-Engine Data Tell Us About the Political Economy of Institutions
Bibliographic record
Abstract
Abstract How does international law affect state behavior? Existing models addressing this issue rest on individual preferences and voter behavior, yet these assumptions are rarely questioned. Do citizens truly react to their governments being taken to court over purported violations? I propose a novel approach to test the premise behind models of international treaty-making, using web-search data. Such data are widely used in epidemiology; in this article I claim that they are also well suited to applications in political economy. Web searches provide a unique proxy for a fundamental political activity that we otherwise have little sense of: information seeking. Information seeking by constituents can be usefully examined as an instance of political mobilization. Applying web-search data to international trade disputes, I provide evidence for the belief that US citizens are concerned about their country being branded a violator of international law, even when they have no direct material stake in the case at hand. This article constitutes a first attempt at utilizing web-search data to test the building blocks of political economy theory.
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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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".