E-Service Research Trends in the Domain of E-Government
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Government ‘e-service’ as a subfield of the e-government domain has been gaining attention to practitioners and academicians alike due to the growing use of information and communication technologies at the individual, organizational, and societal levels. This paper conducts a thorough literature review to examine the e-service research trends during the period between 2005 and 2009 mostly in terms of research methods, theoretical models, and frameworks employed as well as type of research questions. The results show that there has been a good amount of papers focusing on ‘e-Service’ within the field of e-government with a good combination of research methods and theories. In particular, findings show that technology acceptance, evaluation and system architecture are the most common themes, which circa half of the studies surveyed focus on the organizational perspective and that the most employed research methods are case studies and surveys, often with a mix of both types of methodologies.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it