How Much to Share with Third Parties? User Privacy Concerns and Website Dilemmas1
Bibliographic record
Abstract
Publishers websites are increasingly presenting content and services that are not created and managed by the website administrators themselves, but are provided by other third parties. While third party content and services provide value and utility to website users, this comes at the cost of user information being shared with the third party. Privacy concerns surrounding information leakage have been growing rapidly. With increasing concerns regarding online privacy and information disclosure, it is important to understand the factors that affect the level of sharing between publisher websites and third parties. In this study, we propose a two-sided economic model that captures the interaction between the users, publisher websites, and third parties. Specifically, we focus on the effect of privacy concerns on the sharing behavior of the publisher website and the impact of users’ privacy concerns on third party market concentration. We then analyze welfare aspects to provide insights on the impacts of industry regulations and policy on users, publisher websites, and third parties. We partially validate the model using an exploratory empirical analysis of publisher website third party sharing behavior and the structure of the industry. To the best of our knowledge, this study is among the first to analyze publisher website decision making in sharing user information with third parties.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| 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".