GDPR implementation as the main reason for the regional fragmentation in the online mediasphere
Bibliographic record
Abstract
The EU’s General Data Protection Regulation (GDPR) applies not only to the territory of the European Union, but also to all information systems containing data of EU’s citizens around the world. Misusing or carelessly handling personal data bring fines of up to 20 million euros or 4% of the annual turnover of the offending company. This article analyzes the main trends in the global implementation of the GDPR. Authors considered and analyzed results of personal data protection measures in nineteen regions: The USA, Canada, China, France, Germany, India, Kazakhstan, Nigeria, Russia, South Korea and Thailand, as well as the European Union and a handful of other. This allowed identifying a direct pattern between the global tightening of EU’s citizens personal data protection and the fragmentation of the global mediasphere into separate national segments. As a result of the study, the authors conclude that GDPR has finally slowed down the globalization of the online mediasphere, playing a main role in its regional fragmentation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".