Research group on digital government in North America: developing a comparative and transnational agenda
Bibliographic record
Abstract
This poster describes the second year's progress of the research group on Comparative and Transnational Digital Government in North America. This group was formed in 2007 to advance electronic government research across geographic and political boundaries in the region, with the support of the National Science Foundation Digital Government Research Program and the home institutions of the members in Canada, Mexico, and the United States. During the second year of activities, the group members joined their expertise and interests in two face-to-face meetings in Canada. During the second year of work, group members advanced research proposals and developed preliminary products on two topical areas as well as a first draft of a Research Agenda for North America. The group members will continue the discussion of this research agenda in Cholula, Mexico as part of the activities of the 10th Annual International Conference on Digital Government Research (dg.o 2009).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".