Bibliographic record
Abstract
Imagine a consumer using a social media service. They use the application to read news or posts from friends. At the same time, the social media app learns about the consumer by analyzing their profile and monitoring their browsing habits. The more actively the consumer uses the service, the more data the app can collect, which often means a better experience for the consumer and higher revenue for the service. The consumer faces a trade-off: On the one hand, they enjoy the benefits of using social media, such as socializing with friends. On the other hand, the consumer also values their privacy and is concerned about the risk of data leakage, identity theft or other potential abuses of personal information. If the consumer thinks the privacy cost of using the app is high, they will reduce their activity on the platform. This paper develops a dynamic game-theoretic model that analyzes the interaction between a consumer’s incentive to protect privacy and a platform’s incentive to collect data. The platform can encourage the consumer to use the service more actively by employing a certain privacy policy. The key idea is that when the consumer has low privacy on a platform, the marginal cost to the consumer of giving up more privacy is low. Thus, data collection reduces the consumer's welfare but increases their incentive to keep using the service. The paper shows that the platform can collect a lot of data even if the consumer is sensitive to privacy. To do so, the platform initially offers high privacy protection to encourage the consumer to use the platform; however, as time goes by, the platform gradually degrades privacy protection. In the long run, the consumer will lose privacy but keep a high activity level, and the platform typically offers negligible privacy protection.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".