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
Pseudonym technology is attracting more and more attention and, together with privacy violations, is becoming a major issue in various e-services. Current e-service systems make personal data collection very easy and efficient through integration, interconnection, and data mining technologies since they use the user’s real identity. Pseudonym technology with unlinkability, anonymity, and accountability can give the user the ability to control the collection, retention, and distribution of his or her personal information. This chapter explores the challenges, issues, and solutions associated with pseudonym technology for privacy protection in e-services. To have a better understanding of how the pseudonym technology provides privacy protection in e-services, we describe a general pseudonym system architecture, discuss its relationships with other privacy technologies, and summarize its requirements. Based on the requirements, we review, analyze, and compare a number of existing pseudonym technologies. We then give an example of a pseudonym practice — e-wallet for e-services and discuss current issues.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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