Electronic government around the world: Current trends and future prospects
Bibliographic record
Abstract
Abstract In recent decades many countries have leveraged information and communication technologies to facilitate interaction between citizens, businesses and governments. By enhancing government efficiencies and streamlining governance systems, countries expect to strengthen public service deliveries and to improve public and private sector interactions. Open public data is expected to bring better access to information and thus enhance democracy. Despite these promises, electronic government (eGov) policies around the world face challenges brought about by, among other things, inequalities (in terms of abilities, literacy, gender, income, location, age, etc), issues of data quality, as well as privacy and security concerns. eGov can be examined under three different categories: Government‐to‐Government (G2G), Government‐to‐Citizen (G2C), and Government‐to‐Business (G2B). eGov can also be examined via service delivery methodology, based on infrastructure development stages, provider and user perspectives (such as the available eGov services vs. actual eGov usage) or the discursive framing of such plans and programs. This panel addresses several such scenarios to examine the current state of electronic government in various international settings. Panelists will provide insights on specific dynamics in these countries (changing policies, environments, and technologies) and how they relate to successful (or not) e‐government practices. Sponsors SIG III, SIG IFP
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".