A Turning Point in the Oversight of Digital Platforms: A Challenge for American Leadership
Bibliographic record
Abstract
The last quarter of 2020 produced decisions in Brussels, London, and Washington that constitute a turning point in the relationship between the major digital platform companies and democratic societies. Recognizing all the good these companies produce, the different actions share a similar conclusion: that the social costs imposed by the digital companies have become too high. While there may be shared concerns about the unsustainable social and economic costs imposed by the dominant digital companies, the actions by the European Union and United Kingdom have reinforced how the European approaches to the problems of digital platforms are more direct and focused than have been those of the United States. This is a function of two factors. The E.U. and U.K. regulatory culture has been less in thrall to the non-interventionist orthodoxy that has dominated U.S. policy; thus, while the U.S. has effectively turned a blind eye, the Europeans have for several years been searching for effective solutions.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".