Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Bibliographic record
Abstract
Hypertext is an exciting conference because of its ability to bring together researchers and practitioners with a wide range of non-technical and technical interests in the subject of hypertext, including hypertext as art, literary and narrative hypertext, collaborative hypertext, open hypermedia, hypertext as it is implemented by the World Wide Web, and more recently, the Semantic Web. Hypertext 2002 provides many forums to enable this continued interaction including full and short papers, workshops, tutorials, panels, doctoral consortium, and hypertext readings.The two keynote speakers, Ed Ayers and Polle Zellweger, represent the breadth, depth, and maturity of this conference. Dr. Zellweger has been a member of the hypertext community from its inception and has been responsible for many research landmarks during her tenure at Xerox PARC. Her keynote presentation at Hypertext '02 looks at ways to build new agendas for discovery on the first decade and a half. Professor Ayers, recently named Dean of the College and Graduate School of Arts and Sciences at the University of Virginia, has been a pioneer in the application of hypertext to historical research and scholarship. His Valley of the Shadow project has long provided an excellent example for humanists unconvinced of the potential for new technology. Professor Ayers's keynote discusses his current attempt to re-invent the historical research article in natively hypertextual form.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.150 | 0.083 |
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".