Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Bibliographic record
Abstract
It is our pleasure to welcome you to dg.o 2009, the Tenth Annual International Conference on Digital Government Research. The dg.o is unique in its ability to bring together computer and social science researchers, government officials and representatives of industry to investigate how information and communication technologies can be used to make government information, processes and services better, more convenient, and secure, as well as available to all. The dg.o conference is an international conference attracting participants from around the world. For the second year in a row, we are pleased to hold the conference in a country other than US. Dg.o 2008 was held in Montreal, Canada. and we are appreciative of the warm welcome from people and officials in Puebla, Mexico this year. We are extremely fortunate to have two keynote speakers, Beth Noveck and Janice Nall, who have used cutting-edge technology for innovation in public institutions. Their efforts facilitate democratic processes via citizen participation in the patent review process and disease control. The challenges and solutions they pose give us insights into where efforts may be most needed. We are also honored to have a sponsored lunch with a speech from the governor of Guerrero State, Mexico, Zeferino Torreblanca, and we are equally pleased to have a distinguished keynote panel featuring leading Mexican e-government experts, Andres Hoffman, Rodolfo Torres, and Leon David Perez. This year's conference includes thirty five research papers, policy/management papers and student research papers, three of which will be presented in a new Spanish session. This is the first effort to include a language other than English in dg.o. In addition, we have prepared seventeen demos and posters, five panels, one workshop and five tutorials.
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 | 0.011 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.078 |
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".