Privacy-Enhanced Public Name-Authority System for Building Research Communities
Bibliographic record
Abstract
Today, the Internet has become an important source of information about academic researchers and their research activities. The types of information one can obtain from the Internet include contact information, publication information, biographical information, photographs, and other miscellaneous information. Some of this information is generated by professional societies and academic institutions, while other information is generated by individuals and independent enterprises. As the quantity of academic material on the web grows, finding and processing information about a researcher's work is increasingly difficult. For example, it is often hard to discern whether authors of different papers in tangentially-related areas are the same person, based solely on a name. Even if one can determine this information, it is often difficult to assess the accuracy of information obtained, especially if it was generated either by an individual or by community of users. In this thesis, we propose a novel community-based and web-accessible repository of information about academic researchers and their research activities. First, we introduce a web-application called \emph{Federated World Directory of Mathematicians}(FWDM), which retrieves personal information from a variety of disparate data-sets, and which inspired the solutions proposed in this thesis. We then propose a \emph{public name-authority system}, as a means to provide high quality disambiguated information on researchers. The proposed system helps to ensure the quality of information by obtaining only the information approved by the research community. We introduce and describe two approaches to the design of public name-authority systems - the data-filtered and the user-filtered name authority systems - in order to explore their benefits and drawbacks.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".