“Senator . . . I’m Singaporean!”: Privacy Regulation and Data Transfers in Cross-Border Corporations
Bibliographic record
Abstract
A recent congressional hearing involving social media companies, including TikTok and Facebook, made headlines when Senator Tom Cotton of Arkansas grilled the TikTok CEO, Shou Zi Chew, repeatedly asking him if he had ties to China or its Communist Party. The Singaporean CEO, who has served as TikTok’s CEO since 2021, repeatedly replied, “Senator . . . I’m Singaporean!” While Senator Cotton, evoking McCarthy-era sentiments, was severely criticized for his racism and what appears to be a lack of understanding about corporate governance, another problem emerged. TikTok, which is a subsidiary of the Chinese-owned ByteDance, operates in countries around the world and stores its data in Malaysia, Singapore, and the United States. In today’s global privacy landscape, each of these countries has differing privacy laws that, at times, conflict regarding how to handle and transfer data. The lack of consensus on how to store and transfer consumer data exposes corporations to the potential risk of hacking if proper oversight and precautions are not followed. Accordingly, with data becoming a new global currency for expanding businesses, governments must work together to find a solution that streamlines the handling, storage, and transfer of data. Using the United States-Mexico-Canada Agreement as a baseline to create a cross-border data transfer treaty, this Article proposes a multilateral agreement akin to the General Data Protection Regulation to protect consumer data and remove confusion about conflicts of law.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| 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".