Proceedings of the 2008 international conference on Digital government research
Bibliographic record
Abstract
Welcome to dg.o 2008 --- The Ninth Annual International Conference on Digital Government Research! Since you are here, you already know that dg.o meetings offer a unique environment in which computer and social science researchers, government officials and industry representatives come together to share new research in the realm of digital government. We provide a diverse gathering of minds with a common interest in furthering the development of democratic digital government. The conference had its genesis in 1999 as an assembly of researchers receiving support from the U.S. National Science Foundation. Since that time the conference has expanded its attendance beyond NSF funded researchers, has grown in size, and has truly become international in scope. This year is the first time the conference has been held outside the U.S. Montreal's international character is perhaps the perfect site for such an international forum, with presenters traveling from twelve countries and representing dozens of academic institutions, government agencies, and nongovernmental organizations. This year the conference theme Partnerships for Public Innovation focuses on information-intensive innovations in the public sector that involve linkages among government, universities, NGOs, and businesses. This theme emphasizes the importance of sharing practical issues, policy perspectives, research insights, and expert advice, in order to reach higher levels of performance in diverse public enterprises. Our Keynote speakers include: Andy Stein, from the city of Newport News, Virginia, who will be discussing collaborative software ecosystems; Edwin Lau, from the OECD, who leads the OECD E-Government Project; and Dan Chenok, formally of the U.S. Office of Management and Budget, who will be discussing citizen engagement. Even here you can see the range of participation --- from local, through national, to international. In the sessions you will see many disciplines represented, from computer science to the social sciences. You will see theoretical work and applied work. For the first time we have identified case studies, which describe specific field work, as we work to demonstrate the impact of digital government in practice. We will leave it to you to peruse the program and select the sessions that align with your interests.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.257 | 0.141 |
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".