Global Regulations for a Digital Economy: Between New and Old Challenges
Bibliographic record
Abstract
Abstract Digital technologies are often described as posing unique challenges for public regulators worldwide. Their fast‐pace and technical nature are viewed as being incompatible with the relatively slow and territorially bounded public regulatory processes. In this paper, we argue that not all digital technologies pose the same challenges for public regulators. We more precisely maintain that the digital technologies’ label can be quite misleading as it actually represents a wide variety of technical artifacts. Based on two dimensions, the level of centralization and (im)material nature, we provide a typology of digital technologies that importantly highlights how different technical artifacts affect differently local, national, regional and global distributions of power. While some empower transnational businesses, others can notably reinforce states’ power. By emphasizing this, our typology contributes to ongoing discussions about the global regulation of a digital economy and helps us identify the various challenges that it might present for public regulators globally. At the same time, it allows us to reinforce previous claims that these are importantly, not all new and that they often require us to solve traditional cooperation problems.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".