How Much to Share with Third Parties? User Privacy Concerns and Website Dilemmas1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it