Regulating Online Behavioral Advertising, 44 J. Marshall L. Rev. 899 (2011)
Bibliographic record
Abstract
Online behavioral advertising ("OBA"), sometimes known as profiling or behavioral targeting, can be used by on-line publishers and internet marketers to increase the efficiency and effectiveness of their advertising campaigns.'OBA works by collecting data on a user's behavior on the Internet including browsing habits, search queries, and web site viewing history.OBA generally seeks to increase the relevance of advertising displayed to the user, based on data collected about the user, with the aim of increasing the strength of the connection between advertising efforts and purchasing behavior.Recently, the Federal Trade Commission ("FTC"), the Department of Commerce ("DOC"), and congressional leaders have suggested a need for more intensive regulation of OBA.The chief objective of such regulation is to ensure that consumer privacy is protected and that abuses of consumer information do not occur.Others have suggested that self-regulation, or a system of public and private litigation aimed at addressing excesses in OBA practices, may better address these central concerns while maintaining the economic viability of OBA.This Article examines such regulatory efforts and suggests that they illustrate some of the key issues of national regulatory policy, including questions regarding the best means to balance evolving notions of privacy against the similarly dynamic needs of our information-based economy. I.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.022 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".