European Data Protection Regulation and Online New Media: Mind the Enforcement Gap
Bibliographic record
Abstract
Data Protection Authorities (DPAs) play a critical role in shaping and applying the regulation applicable to online media expression within the European Economic Area. Drawing on seven ubiquitous types of online new media actors, a comprehensive survey of these authorities was undertaken. It found that European DPAs generally adopt an expansive interpretation of data protection and a constrained understanding of freedom of expression in this space. In contrast, data protection enforcement is weak and lacking in harmonization. Except for street mapping services, each type of online media actor had only faced relevant enforcement action from a minority of these agencies. DPA financial resourcing is very limited. Notwithstanding the development of DPA ‘network governance’, only DPAs with a particularly extensive interpretative stance proved likely to have engaged in extensive enforcement activity. It remains unclear what difference the General Data Protection Regulation will make to resolving this enforcement gap and its related problems.
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 | 0.090 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.011 |
| 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".