Internet Balkanization gathers pace: is privacy the real driver?
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
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".