Restoring Consumer Sovereignty: How Markets Manipulate Us and What the Law Can Do About It, by Adrian Kuenzler
Bibliographic record
Abstract
Restoring Consumer Sovereignty, by Adrian Kuenzler, is a rich text spanning antitrust, intellectual property (“IP”), and consumer law. Kuenzler, an Assistant Professor in the Faculty of Law at the University of Zurich, is in an authoritative position to comment on the law’s role in stimulating new economic growth, drawing on his own insights from the behavioural sciences. He attributes the preparation of this book to his studies at Zurich University School of Law, his writing on the history of European Competition Law at the European University Institute in Florence, and his time at Yale Law School. In addition to his book, he has contributed other work to the antitrust, intellectual property, and consumer law fields, such as his article, “Promoting Access over Ownership: Realigning Antitrust and Intellectual Property Law to Usher in an Era of Collaborative Consumption.” Parts of chapters two, three, and four of the book also appear in the article, “Dilution Law, Vertical Agreements, and the Construction of Consumption.”
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".