Proceedings of the 2007 ACM symposium on Document engineering
Bibliographic record
Abstract
We welcome you to the Seventh ACM Symposium on Document Engineering -- DocEng 2007, which is being held August 28-31, 2007, at the University of Manitoba in Winnipeg. Always an eclectic symposium, the content of this year's conference emphasizes the increasing breadth associated with the term document. From banknotes to broadcasting, document engineering research is expanding and evolving: the combination of variable data printing and Moore's Law enabling increased document customization and multimedia composition. The program underscores this diversity, with research papers on topics such as multimedia standards, layout, classification, transformation, and paper-electronic document versioning. For DocEng 2007, we received 44 full paper submissions of which 17 were accepted (39%) and 32 short paper submissions of which 16 were accepted (50%). We also accepted 6 of 12 (50%) of the working session, poster and demonstration submissions. All of these contributions were thoroughly refereed, with an average of more than four reviews for the full and short paper submissions. This is the first time that the ACM DocEng symposium has been held in Canada, and we are thus especially pleased to have two Canadian keynote speakers, one from industry and the other from academia, as a highlight. Dr. Margaret-Anne (Peggy) Storey is providing a keynote talk that discusses the role of visualization tools to support the navigation of computer-based information landscapes, particularly in medical informatics and software engineering. Dr. Sara Church provides a keynote talk on the design, function, form, manufacture and security requirements of banknotes. DocEng 2007 also features a Working Session on Document Engineering Education chaired by Ethan Munson, which will allow educators to share teaching content and approaches for undergraduate and graduate education.
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 | 0.005 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.124 | 0.081 |
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".