Proceedings of the 21st ACM conference on Hypertext and hypermedia
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 21st ACM Conference on Hypertext and Hypermedia -- HYPERTEXT 2010. This year's conference continues the tradition of serving as the main venue for high quality, peer-reviewed, double-blind research on linking and interconnectivity, a tradition that now spans over two decades. The Web, the Semantic Web, Web 2.0 and Social Networks all demonstrate the value of the link concept. The conference continues to be an archival source for the latest work relating to hypertext, with a growing body of work that examines not only links between texts, documents, and media, but also between people. The Hypertext 2010 call for papers attracted 90 submissions from around the world, of which 33 were accepted, included 18 long papers and 15 short papers. The program committee accepted papers on a wide variety of topics, ranging from literary hypertext and hypertext theory to social networking and adaptive hypermedia. The program includes the opening keynote speech by Professor Andrew Dillon, Dean of the University of Texas I-School, and the closing keynote by IBM Fellow Dr. Irene Greif. In addition, the conference features four workshops, two panels, 20 posters, and 3 demos. We expect that these proceedings will serve as a valuable reference for those interested in hypertext research.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.191 | 0.106 |
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".