Googling the WTO: What Search-Engine Data Tell Us About the Political Economy of Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it