Internet Balkanization gathers pace: is privacy the real driver?
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
‘[W]e do not really trust the Data Acts in other countries or … we understand that there are none at all. So we feel unprotected in those countries with our data – walking down Fifth Avenue in our underwear’. Provocative exclamations of distrust have become commonplace in recent skirmishes between the EU and the USA over data privacy and trade policy. This is, however, well-trodden ground. Indeed, the statement above was made in the late 1970s by Kerstin Amer, an Under Secretary of State in the Swedish Government, as a justification for the world's first national data protection law, a statute which included a requirement that prior authorization be obtained for exports of personal data. During the 1970s and early 1980s various other countries also raised concerns about ‘data sovereignty’. Not all were European, though several appear to have been motivated by anxiety about a US hegemony that was already emerging in cross-border data services. For example, a 1972 Canadian Federal Government report entitled Computers and Privacy acknowledged that ‘as a sovereign state, Canada feels some national embarrassment and resentment over increasing quantities of often sensitive data about Canadians being stored in a foreign country’. With the benefit of hindsight, this juxtaposition of injured sovereignty and privacy concerns looks like an early example of confused thinking about data export controls. A few years later, the Brazilian Government declared its commitment “to maximize the information resources located in Brazil, declaring that ‘teleprocessing services provided by means of computers located abroad are not, in principle, used by Brazil”.’1
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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