Running out the digital clock: transatlantic privacy politics and veto points in time
Bibliographic record
Abstract
Why does the European Commission continue to sign onto unstable international data flow agreements with the United States? After two agreements were struck down by the European Court of Justice, and clear signals that a new one could meet a similar fate, the European Commission agreed to the Data Privacy Framework. To make sense of this behavior, we connect traditional work on veto points with work on historical institutionalism to highlight an international negotiation strategy – running out the clock. While traditional veto points literature suggests multiple institutions with varying preferences will limit the set of potential policies or their adoption, we highlight how these dynamics change when considering veto points in time. We showcase our argument by building three historical narratives detailing the negotiation of the successive data flow agreements. Our findings have important implications for the future of the transatlantic privacy regime as well as negotiation dynamics.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".