If Not Here, Where?: Transnational Litigation Against U.S. Tech Giants Around the World
Bibliographic record
Abstract
In recent suits around the world against U.S. tech giants-e.g., litigation in Canada against Twitter (now X), in Kenya against Facebook, and in Europe against Google-plaintiffs urge foreign courts to adapt concepts like specific personal jurisdiction in flexible ways to allow litigation to proceed. In their defense, the U.S. companies are reusing the argument that similarly situated defendants successfully deployed in U.S. courts over the last few decades-that the cases are too foreign and do not belong in these courts. But these defendants have lost their home court advantage. They find themselves in courts with closer ties to the disputes at issue, and with stronger claims to both judicial jurisdiction and the authority to apply local substantive law. These companies then find themselves subject to liability-and potentially to remedies with worldwide effect. As specific jurisdiction concepts develop around the world and adjust to modern technological realities, the United States finds itself on the narrower and more old-fashioned end of the spectrum. These trends showcase the importance of comparative law in understanding and developing both U.S. and foreign law in the transnational dispute resolution system-themes that have informed Linda Silberman's scholarship for decades.
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.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.015 | 0.007 |
| 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".