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.
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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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.040 | 0.024 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".