User Attitudes to E-Government Citizen Services in Europe
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
In 2005, the eUSER project undertook a questionnaire survey covering about 10,000 households in 10 European Union member states, the purpose of which was to provide some of the first systematic evidence in Europe of citizen user behaviour and their attitudes to the use of public services, and particularly the role of e-services in this context. The survey focused on a number of themes — the public’s use of government services, the different channels (or media) employed, the nature of potential future demand for e-government, the barriers and experiences in using e-government, and the socio-economic attributes of e-government users compared with non-users. The results provide important new information on the role that the Internet is now playing in the delivery and take-up of government services by European citizens. Face-to-face contact is still the most important channel for contacting government in Europe. In some countries (e.g., the UK), however, telephone and post have overtaken face-to-face. Results also show that potential demand for e-government services is about 50% of all government users and could be higher. One quarter of individual e-government users have acted as intermediaries for family members or friends, and one quarter have also done so on behalf of their employer. Most barriers that users anticipate they will meet when using e-government relate to difficulty in actually starting, with a feeling that face-to-face is better and the fear about data privacy important. However, once citizens have used e-government services, the barriers appear less, though still important, and relate mainly to the difficulty of feeling left alone with problems or questions.
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.
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 | 0.005 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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