Bibliographic record
Abstract
Antitrust theory portrays data privacy as a factor, like quality, that improves with competition. This Essay argues that view is an incomplete account of the new interface between antitrust and data privacy. The more complex reality is that, over the last twenty-five years, data privacy has also become a separate area of legal doctrine. In that capacity, data privacy law may clash at the margins with antitrust—much like intellectual property or consumer protection law did before it. The Essay sheds new light on this tension at the interface of antitrust and data privacy. It provides a descriptive, historical and comparative account of the friction emerging between these areas of law in the digital economy, where data access can both drive competition and reduce privacy. The Essay then lays out a new approach to analyze claims of conflicting data privacy and competition interests, one that emphasizes the accommodation of both areas of law.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.025 | 0.050 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".