Comments on 'Information Privacy and Innovation in the Internet Economy'
Bibliographic record
Abstract
These comments are prepared with the aim of clarifying the contribution, insights, and context of the academic research we have done in the area of digital privacy. We have submitted a similar document in response to the FTC Staff Report on ‘Protecting Consumer Privacy in an Era of Rapid Change: A Proposed Framework for Businesses and Policymakers’. In terms of the specific questions asked in the ‘Information Privacy and Innovation in the Internet Economy ’ document, our research provides insight into (1) ’What is the best way of promoting transparency so as to promote informed choices?’, and (2) ‘Are there lessons from sector-specific [other] privacy laws–their development, their contents, or their enforcement–that could inform U.S. commercial data privacy policy?’ 1 Effect of regulation on the advertising industry Goldfarb and Tucker (2011c) uses nearly 10,000 randomized field or ‘a/b ’ tests of online advertising and subsequent survey responses by three million consumers to investigate how advertising effectiveness changed in Europe following the enactment of the 2002 E-Privacy Directive. We wanted to clarify three additional insights that we feel this paper provides.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".