Bibliographic record
Abstract
Information about individuals is currently maintained in many thousands of databases, with much of that information, such as name and address, replicated across multiple databases. However, this proliferation of personal information raises issues of privacy for the individual, as well as maintenance issues in terms of the accuracy of the information. Ideally, each individual would own, maintain and control his personal information, allowing access to those who needed at the time it was needed. Organizations would contact the individual directly to obtain information, therefore being assured of using current and correct information.While research has been performed on users owning and controlling access to their personal information in an electronic commerce environment, we argue that this concept should be extended to all user information including, for example, medical and financial information. The end goal is not for users to simply maintain copies of this information, but to be the source of this information.This paper presents the concept of users owning their personal information and introduces some of the issues involved in users being able to control access to this information. The security requirements, including authentication, access control and audit, as well as user interfaces and trust, for this new paradigm are given particular emphasis.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.038 |
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".