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
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..
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it