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Record W1492420096 · doi:10.5555/1248460.1248537

Working group on comparative and transnational digital government in North America

2007· article· en· W1492420096 on OpenAlexaboutno aff
J. Ramón Gil-García, Natalie Helbig, Theresa A. Pardo, Luis F. Luna‐Reyes, Celene Navarrete

Bibliographic record

VenueInternational Conference on Digital Government Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsDigital governmentGovernment (linguistics)Variety (cybernetics)Public administrationPolitical sciencePublic relationsPoliticsComparative researchFocus groupComparative politicsPublic policySociologyDigital transformationSocial scienceComputer science

Abstract

fetched live from OpenAlex

This poster describes the research plans of the working group on Comparative and Transnational Digital Government in North America. This group is partially funded by the National Science Foundation Digital Government Research Program to advance e-government research across geographic and political boundaries. Researchers from a variety of institutions and disciplines will join their expertise and interests to develop a comparative and transnational research agenda targeted at questions about intergovernmental digital government initiatives in Canada, Mexico, and the United States. Multi-jurisdictional policy domains such as public health and safety will be the primary focus of study. The products from this collaboration will provide resources for academics and practitioners throughout North America and other regions in the world. A long-term research and practice collaboration among researchers is also expected to emerge from this interaction.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.155
GPT teacher head0.404
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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