Bibliographic record
Abstract
As transborder data flows of personal data are increasing in volume and frequency, a jurisdiction's capacity to enforce personal data protection laws outside its territory is becoming more necessary and more difficult. As is shown in this paper, there have been three main approaches to dealing with this issue: the jurisdiction-to-jurisdiction, organization-to-organization, and the data localization approaches. While the jurisdiction-to-jurisdiction approach makes transborder data flows contingent upon the existence of adequate/equivalent national data protection laws, the organization-to-organization approach makes it the responsibility of individual data controllers to meet basic standards of data protection when those data are processed offshore. The data localization approach on the other hand, obliges third parties to store personal data within the boundaries of the country of operation. The fact that each of these models has its strengths and weaknesses, and that different jurisdictions have adopted different approaches based on different motivations and interests, makes the actual pursuit of international data protection increasingly complex..
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".