Proceedings of the 2004 ACM symposium on Document engineering
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 4th <i>ACM Symposium on Document Engineering - DocEng 2004</i>. This year's symposium continues its tradition of being a premier forum for presentation of research results and experience reports on leading edge issues of document engineering, including models, systems, applications, and theory. The mission of the symposium is to share research results and to identify new directions for future research and development. DocEng 2004 gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the many aspects of document engineering, ranging from image analysis up to structured content transformation and usability studies. The call for papers attracted 86 submissions (53 long papers and 33 short papers) from Asia, Canada, South America, Europe and the United States. The program committee accepted 22 full papers (10 pages) and 12 short papers (3 pages) that cover a great variety of topics, according to our "document-in-the-large" culture. In addition, the program includes a keynote speech by Kristoffer Rose from IBM J. Watson Research Lab, who will outline an ambitious and original vision of XML document processing and offer useful insights on forthcoming trends. We hope that these proceedings will serve as a valuable reference for document researchers and developers worldwide. Putting together <i>DocEng 2004</i> was a team effort. First of all, we would like to thank the authors and keynote speaker for providing the content of the program. We would like to express our gratitude to the program committee and external reviewers, who worked very hard in reviewing papers and providing suggestions for their improvements. We would also like to thank Andrian Marcus and Andrey Sergeyev for designing and maintaining of the <i>DocEng 2004</i> web site, with a special thank to Andrey who greatly helped realizing the front cover of these proceedings. Finally, we would like to thank our sponsor, ACM SIGWEB, and the Xerox Research Centre Europe for their concrete support of this meeting. We hope that you will find this program interesting and thought-provoking and that the symposium will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.115 | 0.070 |
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".