Bibliographic record
Abstract
Pseudonyms are of course nothing new under the sun. We know of them in a good many and diverse range of figures, including resistance fighters, saboteurs, gangsters, heroes, vulnerable parties to a lawsuit, authors, and actors. However, behind this surface familiarity is a very complex phenomenon. And it appears increasingly complex as one takes into account the proliferation of use as facilitated by rapidly developing information and communication technologies. In this paper we canvass the main areas of law in which the appears and extract and explicate key legal principles and considerations. This legal analysis is augmented by consideration of social, cultural, and political dimensions of naming practices. We survey the phenomenon of use to reveal a vast variety of different uses of the pseudonym, for different purposes, and under different conditions. We propose a conceptual framework for managing the multiplicity of meanings that the term pseudonym has taken on in use today. This framework, we believe, is useful not only for better understanding what is going on in the phenomenon of pseudonymity today but also for normative analysis, discussion, and debate about how law and public policy should approach the pseudonym.
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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".